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Florida State University Libraries
Electronic Theses, Treatises and Dissertations The Graduate School
2013
The Description and Indexing of EditorialCartoons: An Exploratory StudyChristopher Ryan Landbeck
Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected]
THE FLORIDA STATE UNIVERSITY
COLLEGE OF COMMUNICATION AND INFORMATION
THE DESCRIPTION AND INDEXING OF EDITORIAL CARTOONS: AN EXPLORATORY
STUDY
By
Christopher Ryan Landbeck
A Dissertation submitted to the
School of Library and Information Studies
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Spring Semester, 2013
ii
Chris Landbeck defended this dissertation on January 16, 2013.
The members of the supervisory committee were:
Corinne Jörgensen
Professor Directing Dissertation
Lois Hawkes
University Representative
Michelle Kazmer
Committee Member
Paul Marty
Committee Member
Besiki Stvilia
Committee Member
The Graduate School has verified and approved the above-named committee members, and
certifies that the dissertation has been approved in accordance with university requirements.
iii
I dedicate this to my wife, Rebekah Sariah Landbeck.
Even when it’s bad, it’s better than most.
iv
ACKNOWLEDGEMENTS
I would like to acknowledge the following people as integral to the completion of this work:
Corinne Jörgensen; whose time and effort have not gone unnoticed;
Casey McLaughlin; whose help with the steve.tagger software was crucial to this work;
Nicole Alemanne; whose pointing out of certain mistakes proved to be a lifesaver;
Mai Lustria; whose example I will follow in many, many ways;
David Miner; whose counsel and wisdom kept me on the right path;
Diane Rasmussen; whose insights and ear helped me in times of uncertainty;
And Gary Van Osdell; whose offhand comment “History majors can always become librarians”
led me to where I am.
v
TABLE OF CONTENTS
Table of Contents ............................................................................................................................ v
List of Tables .................................................................................................................................. x
List of Figures ................................................................................................................................ xi
Abstract ........................................................................................................................................ xiii
1 Introduction .................................................................................................................................. 2
1.1 Background of the Problem ................................................................................................ 2
1.2 Statement of the Problem .................................................................................................... 2
1.3 Purpose of the Study ........................................................................................................... 2
1.4 Research Questions ............................................................................................................. 3
1.4.1 How are editorial cartoons described in a tagging environment? And how do those
tags fall into Jörgensen’s 12 Classes of image description? ............................................... 3
1.4.2 How are editorial cartoons described in a simulated query environment? And how
do those tags fall into Jörgensen’s 12 Classes of image description? ................................. 3
1.4.3 How do the tagging terms compare to the querying terms? ...................................... 4
1.4.4 How might these findings affect the practices of both editorial cartoonists and
image professionals? ........................................................................................................... 4
1.5 Importance of the Study ...................................................................................................... 4
1.6 Scope of the Study .............................................................................................................. 5
1.7 Definition of Terms............................................................................................................. 5
1.7.1 Cartoon vs. comic ...................................................................................................... 5
1.7.2 Editorial vs. political .................................................................................................. 6
1.8 Limitations .......................................................................................................................... 6
2 Literature Review......................................................................................................................... 8
2.1 Examination of the problem................................................................................................ 8
2.1.1 Editorial cartoons: Indexing and Interpretation ......................................................... 8
2.1.1.1 Cartoons themselves ......................................................................................... 9
2.1.1.2 Problems in cartoon interpretation .................................................................. 12
2.1.2 Examples of cartoon collections .............................................................................. 15
2.1.2.1 Sources ............................................................................................................ 15
2.1.2.2 Resources ........................................................................................................ 17
2.2 Conceptual Basis ............................................................................................................... 21
2.2.1 Theory: Panofsky, iconography, and Shatford-Layne ............................................. 21
2.2.1.1 Panofsky’s iconology ...................................................................................... 22
2.2.1.2 Panofsky and Shatford-Layne ......................................................................... 25
2.2.2 Image indexing......................................................................................................... 28
2.2.2.1 Indexing Considerations ................................................................................. 28
2.2.2.2 Concerns ......................................................................................................... 32
2.2.2.3 User Behavior ................................................................................................. 41
2.2.2.4 Domain-based approach.................................................................................. 44
2.2.2.5 Jörgensen’s 12 Classes .................................................................................... 47
2.2.2.5.1 The Classes. ........................................................................................... 47
2.2.2.5.2 Description. ............................................................................................ 49
2.2.2.5.3 Queries. .................................................................................................. 50
2.3 Practical Applications ....................................................................................................... 51
vi
2.3.1 Metadata ................................................................................................................... 52
2.3.1.1 Metadata as a concept ..................................................................................... 53
2.3.1.2 Metadata – types and functions....................................................................... 55
2.3.1.3 Metadata schema ............................................................................................. 57
2.3.1.4 Current Relevant Metadata Schema................................................................ 58
2.3.2 Folksonomies ........................................................................................................... 62
2.3.2.1 Definitions....................................................................................................... 62
2.3.2.2 Criticisms ........................................................................................................ 63
2.3.2.3 User Behavior ................................................................................................. 65
2.4 Not relevant at this time .................................................................................................... 68
2.4.1 Cataloging ................................................................................................................ 68
2.4.2 Archiving ................................................................................................................. 69
2.4.3 Information Retrieval ............................................................................................... 69
2.4.4 Content-based image retrieval ................................................................................. 70
2.4.5 Word and Image Studies .......................................................................................... 70
2.4.6 Research simply about cartoons............................................................................... 71
3 Methodology .............................................................................................................................. 72
3.1 Overview ........................................................................................................................... 72
3.2 Research Questions ........................................................................................................... 72
3.2.1 How are editorial cartoons described in a tagging environment, and how do the
resulting tags map into Jörgensen’s 12 Classes? .............................................................. 72
3.2.2 How are editorial cartoons described in a simulated query environment, and how do
query keywords and phrases fall into Jörgensen’s 12 Classes? ........................................ 73
3.2.3 How do the tagging terms compare to the simulated query terms? ......................... 73
3.2.4 How might these findings affect the practices of both editorial cartoonists and
image professionals? ......................................................................................................... 73
3.3 Data collection .................................................................................................................. 74
3.3.1 Population ................................................................................................................ 74
3.3.1.1 Tagging and query activities ........................................................................... 74
3.3.1.1.1 Degree holding population. .................................................................... 74
3.3.1.1.2 Non-degree holding population. ............................................................ 75
3.3.1.2 Interviews ........................................................................................................ 76
3.3.2 Sampling .................................................................................................................. 76
3.3.2.1 Tagging and query activities ........................................................................... 76
3.3.2.2 Interviews ........................................................................................................ 76
3.3.3 Description of data gathering environment .............................................................. 77
3.3.3.1 Images ............................................................................................................. 77
3.3.3.2 Tagging environment ...................................................................................... 78
3.3.3.3 Simulated query environment ......................................................................... 79
3.3.3.4 Interview environment .................................................................................... 80
3.3.4 Subject activity......................................................................................................... 80
3.3.4.1 Informed consent and opting in ...................................................................... 80
3.3.4.2 Demographic information ............................................................................... 81
3.3.4.3 Tagging activity .............................................................................................. 81
3.3.3.4 Simulated query activity ................................................................................. 81
3.3.3.5 Post-results interviews .................................................................................... 82
vii
3.4 Data Analysis .................................................................................................................... 83
3.4.1 Tagging Activity ...................................................................................................... 83
3.4.1.1 Tag analysis .................................................................................................... 83
3.4.1.1.1 Review of practice – tags. ...................................................................... 86
3.4.1.2 Tag comparison ............................................................................................... 86
3.4.2 Simulated query Activity ......................................................................................... 87
3.4.2.1 Query Analysis................................................................................................ 87
3.4.2.1.1 Review of practice—queries. ................................................................. 87
3.4.2.2 Word and phrase comparison.......................................................................... 88
3.4.3 Tag-simulated query comparison ............................................................................. 88
3.4.4 Interview analysis .................................................................................................... 88
3.5 Validity and Reliability ..................................................................................................... 89
3.5.1 Validity .................................................................................................................... 89
3.5.2 Reliability ................................................................................................................. 90
3.6 Limitations ........................................................................................................................ 91
3.7 Ethical and legal concerns ................................................................................................ 93
3.7.1 Ethical concerns ....................................................................................................... 93
3.7.2 Legal concerns ......................................................................................................... 94
4 Results ........................................................................................................................................ 95
4.1 Tagging phase ................................................................................................................... 95
4.1.1 Participants ............................................................................................................... 95
4.1.2 Tagging results ......................................................................................................... 95
4.1.3 Results – Tagging Phase .......................................................................................... 96
4.1.3.1 – Image “ande1” ............................................................................................. 96
4.1.3.2 – image “bree1” .............................................................................................. 98
4.1.3.3 – image “hand1” ............................................................................................. 99
4.1.3.4 – image “luck1” ............................................................................................ 101
4.1.3.5 – image “rame1” ........................................................................................... 103
4.1.3.6 – image “ande2” ............................................................................................ 104
4.1.3.7 – image “bree2” ............................................................................................ 106
4.1.3.8 – image “hand2” ........................................................................................... 108
4.1.3.9 – image “luck2” ............................................................................................ 109
4.1.3.10 – image “rame2” ......................................................................................... 111
4.1.3.11 Review of tags by outside reviewer ............................................................ 113
4.1.4 Summary of results: Tagging phase ....................................................................... 113
4.2 Query phase .................................................................................................................... 118
4.2.2 Participants ............................................................................................................. 118
4.2.2 Query results .......................................................................................................... 118
4.2.3 Results – Query Phase ........................................................................................... 119
4.2.3.1 – image “ande1” ............................................................................................ 119
4.2.3.2 – image “bree1” ............................................................................................ 120
4.2.3.3 – image “hand1” ........................................................................................... 122
4.2.3.4 – image “luck1” ............................................................................................ 124
4.2.3.5 – image “rame1” ........................................................................................... 125
4.2.3.6 – image “ande2” ............................................................................................ 127
4.2.3.7 – image “bree2” ............................................................................................ 129
viii
4.2.3.8 – image “hand2” ........................................................................................... 130
4.2.3.9 – image “luck2” ............................................................................................ 132
4.2.3.10 – image “rame2” ......................................................................................... 134
4.2.3.11 Review of queries by outside reviewer ....................................................... 135
4.2.4 Summary of results: Query phase .......................................................................... 136
4.3 Comparison of results ..................................................................................................... 141
4.3.1 Comparisons within this Research ......................................................................... 141
4.3.2 Comparisons to the Literature ................................................................................ 142
4.3.3 Post hoc observations ............................................................................................. 143
4.4 Interviews ........................................................................................................................ 147
4.4.1 Interviewees ........................................................................................................... 148
4.4.2 Central interview questions.................................................................................... 149
4.4.2.1 Pre-results predictions ................................................................................... 150
4.4.2.2 Post-results comparison ................................................................................ 152
4.4.2.3 Effects of data on practice ............................................................................. 153
5 Discussion, Implications, & Conclusions ................................................................................ 155
5.1 Discussion ....................................................................................................................... 156
5.1.1 Theory .................................................................................................................... 156
5.1.2 Previous studies of cartoon interpretation.............................................................. 159
5.1.3 Similarities to Resources........................................................................................ 161
5.1.4 Metadata ................................................................................................................. 162
5.1.5 Folksonomies and collaborative technology .......................................................... 165
5.2 Implications..................................................................................................................... 166
5.2.1 For society .............................................................................................................. 166
5.2.2 For library and information studies........................................................................ 167
5.2.3 For editorial cartoons ............................................................................................. 168
5.3 Future Work .................................................................................................................... 169
5.3.1 Corrections ............................................................................................................. 169
5.3.1.1 Jörgensen’s 12 Classes .................................................................................. 171
5.3.1.2 Heterogeneous image sets ............................................................................. 174
5.3.1.3 Confidence in tags......................................................................................... 176
5.3.2 Supplementary studies ........................................................................................... 177
5.3.2.1 Effect of time on cartoon interpretation ........................................................ 177
5.3.2.2 Effect of time on recall ................................................................................. 178
5.3.2.3 Personal agreement and describing behavior ................................................ 178
5.3.2.4 Supplemental data ......................................................................................... 178
5.3.3 Practical application ............................................................................................... 179
5.4 Conclusions ..................................................................................................................... 181
5.4.1 How do the tagging terms compare to the querying terms? .................................. 181
5.4.2 How are editorial cartoons described in a tagging environment and a simulated
query environment? And how do those tags fall into Jorgensen’s 12 Classes of image description? ..................................................................................................................... 182
5.4.2.1 Among similar studies .................................................................................. 182
5.4.2.2 Among dissimilar studies .............................................................................. 188
5.4.3 Demographic variables .......................................................................................... 189
5.4.4 Effects of findings on practice ............................................................................... 190
ix
A Institutional Review Board Approval Memoranda ................................................................. 192
A.1 Initial Approval Memorandum ...................................................................................... 192
A.2 Approval of Amendament Memorandum ...................................................................... 194
A.3 Re-Approval Memorandum ........................................................................................... 195
B Images Used in the Pilot Study ............................................................................................... 196
C Jörgensen’s 12 Classes ............................................................................................................ 199
D Communication and Consent for Tagging and Query Tasks for the Pilot Study ................... 205
D.1 Email to Department Heads of Potential Participants .................................................... 205
D.2 Email to Recruit Participants ......................................................................................... 206
D.3 Consent for Tagging and for Query Activities............................................................... 207
D.4 Screenshots, Tagging Website ....................................................................................... 209
E Communication, Consent, and Script for Interviews for the Pilot Test .................................. 216
E.1 Email to Potential Interviewees ...................................................................................... 216
E.2 Pre-Interview Email (with Jörgensen’s 12 Classes) ....................................................... 218
E.3 Informed Consent and Script for Semi-Structured Interview ......................................... 219
E.4 Screenshots, Query website ............................................................................................ 222
F Pilot Study Recruiting Documentation .................................................................................... 225
G Images used in the full study, by week ................................................................................... 226
G.1: Week 1 (Monday, October 31, 2011) ........................................................................... 226
G.2: Week 2 (Monday, November 7, 2011) ......................................................................... 229
H Screenshots of the revised interfaces ...................................................................................... 232
H.1: Tagging activity ............................................................................................................ 232
H.2: Simulated query activity ............................................................................................... 239
I Raw tagging activity data ......................................................................................................... 245
J Raw query activity data ............................................................................................................ 290
K Interview script ....................................................................................................................... 318
References ................................................................................................................................... 321
Biographical Sketch .................................................................................................................... 332
x
LIST OF TABLES
Table 1 Summary of frequencies for Jörgensen’s 12 Classes across three sets of image – tagging
environment................................................................................................................................... 50
Table 2 Summary of frequencies for Jörgensen’s 12 Classes across three sets of images – query
environment................................................................................................................................... 51
Table 3 Comparison of Metadata Types ...................................................................................... 55
Table 4 Summary data for the tagging phase .............................................................................. 95
Table 5 Classes and attributes for”ande1” – tagging environment ............................................ 96
Table 6 Classes and attributes for”bree1” – tagging environment ............................................ 98
Table 7 Classes and attributes for”hand1” – tagging environment ......................................... 100
Table 8 Classes and attributes for”luck1” – tagging environment ........................................... 102
Table 9 Classes and attributes for”rame1” – tagging environment ......................................... 103
Table 10 Classes and attributes for”ande2” – tagging environment ........................................ 105
Table 11 Classes and attributes for”bree2” – tagging environment ........................................ 107
Table 12 Classes and attributes for”hand2” – tagging environment ....................................... 108
Table 13 Classes and attributes for”luck2” – tagging environment ......................................... 110
Table 14 Classes and attributes for”rame2” – tagging environment ....................................... 112
Table 15 Summary results – tagging phase by Class with percentage of overall total ............. 113
Table 16 Summary data for the query phase ............................................................................. 118
Table 17 Classes and attributes for”ande1” – query environment ........................................... 119
Table 18 Classes and attributes for”bree1” – query environment............................................ 121
Table 19 Classes and attributes for”hand1” – query environment ........................................... 123
Table 20 Classes and attributes for”luck1” – query environment ............................................ 124
Table 21 Classes and attributes for”rame1” – query environment .......................................... 126
Table 22 Classes and attributes for”ande2” – query environment ........................................... 128
Table 23 Classes and attributes for”bree2” – query environment............................................ 129
Table 24 Classes and attributes for”hand2” – query environment ........................................... 131
Table 25 Classes and attributes for”luck2” – query environment ............................................ 133
Table 26 Classes and attributes for”rame2” – query environment .......................................... 134
Table 27 Summary results – query phase by Class with percentage of overall total ................ 136
Table 28 Summary of frequencies for Jörgensen’s 12 Classes across four sets of images in a free-tagging environment ............................................................................................................ 142
Table 29 Summary of frequencies for Jörgensen’s 12 Classes across three sets of images in an image –query environment .......................................................................................................... 143
Table 30 Division of Jörgensen’s 12 Classes by the theories of Panofsky, Shatford-Layne, and
Fidel ............................................................................................................................................ 157
Table 31 Division of Jörgensen’s 12 Classes by the theories of Panofsky, Shatford-Layne, and
Fidel, with results from both activities ....................................................................................... 158
Table 32 Comparison of Jörgensen’s Classes to CDWA Categories ........................................ 164
Table 33 Data from tagging activity ........................................................................................... 245
Table 34 Data from query activity .............................................................................................. 290
xi
LIST OF FIGURES
Figure 1 andi1 [in color] (Anderson, 2011b) ................................................................................ 96
Figure 2 bree1 [in color] (Breen, 2011b) ...................................................................................... 98
Figure 3 hand1 [in color] (Handelsman, 2011b) ......................................................................... 100
Figure 4 luck1 [in color] (Luckovich, 2011b). In the banner, the words “mission” and
“accomplished” are in yellow, whiel the other words are in white. ........................................... 101
Figure 5 rame1 [in color] (Ramirez, 2011b) ............................................................................... 103
Figure 6 ande2 [in color] (Anderson, 2011c).............................................................................. 105
Figure 7 bree2 [in black & white] (Breen, 2011c) ...................................................................... 106
Figure 8 hand2 [in color] (Handelsman, 2001c) ......................................................................... 108
Figure 9 luck2 [in color] (Luckovich, 2011c) ............................................................................. 110
Figure 10 rame2 [in color] (Ramirez, 2011c) ............................................................................. 111
Figure 11 High-mean-low ranges for tagging activity ................................................................ 115
Figure 12 Comparison of tagging behavior by gender, by percent of overall totals .................. 115
Figure 13 Comparison of tagging behavior by political leaning, by percent of overall totals ... 116
Figure 14 Comparison of tagging behavior by education, by percent of overall totals .............. 117
Figure 15 ande1 [in color] (Anderson, 2011b) ........................................................................... 119
Figure 16 bree1 [in color] (Breen, 2011b) .................................................................................. 121
Figure 17 hand1 [in color] (Handelsman, 2011b) ....................................................................... 122
Figure 18 luck1 [in color] (Luckovich, 2011b) In the banner, the words “mission” and
“accomplished” are in yellow, while the other words are in white. ........................................... 124
Figure 19 rame1 [in color] (Ramirez, 2011b) ............................................................................. 126
Figure 20 ande2 [in color] (Anderson, 2011c) ............................................................................ 127
Figure 21 bree2 [in black & white] (Breen, 2011c) .................................................................... 129
Figure 22 hand2 [in color] (Handelsman, 2011c) ....................................................................... 131
Figure 23 luck2 [in color] (Luckovich, 2011c) ........................................................................... 132
Figure 24 rame2 [in color] (Ramirez, 2011c) ............................................................................. 134
Figure 25 High-mean-low ranges for query activity................................................................... 138
Figure 26 Comparison of simulated query behavior by gender, by percent of overall totals ..... 138
Figure 27 Comparison of simulated query behavior by political leaning, by percent of overall
totals ............................................................................................................................................ 139
Figure 28 Comparison of simulated query behavior by education, by percent of overall totals 140
Figure 29 Comparison of frequencies of Class use between the tagging and simulated query
activities. ..................................................................................................................................... 141
Figure 30 Comparison of frequencies among tagging studies, with Classes in alphabetical order
per study ...................................................................................................................................... 144
Figure 31 Comparison of frequencies among tagging studies, with Classes in rank order per
study ............................................................................................................................................ 145
Figure 32 Comparison of frequencies among simulated query studies, with Classes in
alphabetical order per study ........................................................................................................ 146
Figure 33 Comparison of frequencies among simulated query studies, with Classes in rank order
per study ...................................................................................................................................... 147
Figure 34 Tag cloud of interviewee’s predictions ...................................................................... 152
Figure 35 Pilot study image rami0 (Ramirez, 2011a) ................................................................. 196
Figure 36 Pilot study image ande0 (Anderson, 2011a)............................................................... 197
xii
Figure 37 Pilot study image bree0 (Breen, 2001a) ..................................................................... 197
Figure 38 Pilot study image hand0 (Handleman, 2011a) ........................................................... 198
Figure 39 Pilot study image luck0 (Luckovich, 2011a) .............................................................. 198
Figure 40 Screen 1a – welcome page (top) ................................................................................. 209
Figure 41 Screen 1b – welcome page (bottom) .......................................................................... 210
Figure 42 Screen 2 – Registration page ...................................................................................... 211
Figure 43 Screen 3 – Thank You and Instructions page ............................................................. 211
Figure 44 Screen 4 – Tagging start page .................................................................................... 212
Figure 45 Screen 5 – Example of Blank Tagging page .............................................................. 213
Figure 46 Screen 6 – Example of Filled-In Tagging page .......................................................... 214
Figure 47 Screen 7 – Done and Thank You page (Week 1) ....................................................... 214
Figure 48 Screen 8 – Done and Reminder page (Week 2) ......................................................... 215
Figure 49 Screen 1 – Welcome page .......................................................................................... 222
Figure 50 Screen 2 – Query Starting page .................................................................................. 222
Figure 51 Screen 3 – Example of Blank Query page .................................................................. 223
Figure 52 Screen 4 – Example of Filled-In Query page ............................................................. 224
Figure 53 Screen 5 – Thank You page........................................................................................ 224
Figure 54 ande1 [in color] (Anderson, 2011b) ........................................................................... 226
Figure 55 bree1 [in color] (Breen, 2001b) .................................................................................. 227
Figure 56 hand1 [in color] (Handleman, 2011b) ........................................................................ 227
Figure 57 luck1 [in color] (Luckovich, 2011b) .......................................................................... 228
Figure 58 rame1 [in color] (Ramirez, 2011b) ............................................................................. 228
Figure 59 ande2 [in color] (Anderson, 2011c) ............................................................................ 229
Figure 60 bree2 (in black & white) (Breen, 2011c) .................................................................... 229
Figure 61 hand2 [in color] (Handleman, 2011c) ........................................................................ 230
Figure 62 luck2 [in color] (Luckovich, 2011c) ........................................................................... 230
Figure 63 rame2 [in color] (Ramirez, 2011c) ............................................................................. 231
Figure 64 Tagging phase screenshot -- Welcome page (top) ..................................................... 232
Figure 65 Tagging phase screenshot -- Welcome page (bottom) ............................................... 233
Figure 66 Tagging phase screenshot -- registration page ........................................................... 234
Figure 67 Tagging phase screenshot -- instruction page ........................................................... 234
Figure 68 Tagging phase screenshot -- staging area page .......................................................... 235
Figure 69 Tagging phase screenshot -- blank tagging page ...................................................... 236
Figure 70 Tagging phase screenshot -- filled-in tagging page with editing options .................. 237
Figure 71 Tagging phase screenshot -- thank you page, Week 1 .............................................. 238
Figure 72 Tagging phase screenshot -- thank you page, Week 2 .............................................. 238
Figure 73 Query phase screenshot -- welcome page .................................................................. 239
Figure 74 Query phase screenshot -- staging area page .............................................................. 240
Figure 75 Query phase screenshot -- blank query page (top) ..................................................... 241
Figure 76 Query phase screenshot -- blank query page (bottom) ............................................... 242
Figure 77 Query phase screenshot -- filled-in query page with editing options ......................... 243
Figure 78 Query phase screenshot -- thank you page ................................................................. 244
xiii
ABSTRACT
While access to images in general has improved in the last 20 years, due to both advances
in electronic storage and dissemination and to improvements in the intellectual provisions of
them, access to editorial cartoons lags behind access to other types of images. While there have
been piecemeal or ad hoc efforts to organize large cartoon collections, these efforts have been
based on the wants and needs of the organizers, publishers, or collectors. The purpose of this
research was to gather information about user's descriptions of editorial cartoons. Specifically, it
gathered terms and phrases provided by users to describe a set of editorial cartoons, both in an
image tagging environment and in a simulated query environment.
The population for this research was a blended sample; one population consisted of
academics in fields that were assumed to have an interest in the research itself, and who were
seen as likely to give a full, rich description of each image. The second population consisted of
non-degree holding participants, against which the first results could be compared. The images
used in this study were political cartoons from the five most recent Pulitzer Prize-winning
editorial cartoonists. Content analysis of the cartoons’ descriptions placed each description’s
components into one of Jörgensen’s 12 Classes of image description, and the frequencies of each
Class in this study were compared to similar studies.
The results of this research show that while editorial cartoons can be described using
Jörgensen’s 12 Classes, they are described in very different ways than are other images. It was
found that the Class ABSTRACT CONCEPTS was far more dominant when describing and searching
for editorial cartoons than was so for other types of images; the Class LITERAL OBJECT was
dominated by the attribute Text in both scenarios; VIEWER REACTIONS play a far larger role for
these images than for others; and four Classes that are at least somewhat useful in searching for
other types of images were almost unused when searching for editorial cartoons. Demographic
variables show major differences in behavior among those of different education levels in
tagging, and among different political views and genders when querying. Confirmatory
interviews with image professionals and editorial cartoonists showed that the results would be of
some use when implemented in the field. The results of this research would help inform efforts
to index any image where the meaning of it was more important than the image content, and may
help to describe all types of non-textual records of history and commentary.
2
CHAPTER 1
INTRODUCTION
1.1 Background of the Problem
While access to images in general has improved in the last 20 years, due to both advances
in electronic storage and dissemination and to improvements in the intellectual access to them,
access to editorial cartoons lags behind access to other types of images. The Library of Congress
(2009) houses the largest collection of editorial cartoons in the world, but provides uneven
access to them, describing some images in great detail and others with very little. The
Doonesbury Collection (Trudeau, 1998) provides access to several series of strips, but clutters
this access with several extraneous kinds of information. The New Yorker collection of cartoons
(Mankoff, 2004) states that its collection was described on an ad hoc basis, reflecting terms
popular in the everyday language of the time but of limited utility to following generations. The
CNN archive of political cartoons (2009) provides access only by date. And while it is true that
some small and limited-scope cartoon collections describe their contents well (Bachorz, 1998;
Mandeville, 2009), their methods have not been implemented in such a way as to gauge their
usefulness in a large collection. Compare these to ARTstor (2011), Corbis Images (2011), and
the Getty (2011) and Guggenheim (2011) imagebases, and a gap in coverage, treatment, and
research become evident.
1.2 Statement of the Problem
There has been more work done in describing and providing access to other kinds of
images than there has been for editorial cartoons. While there have been piecemeal or ad hoc
efforts to organize large cartoon collections, these efforts have been based on the wants and
needs of the organizers, publishers, or collectors. We know little concerning the habits and
expectations of users vis-à-vis editorial cartoons, and there has not been an organized, user-based
approach to providing access to these kinds of images. The gap in knowledge addressed in this
study is that which exists between what we know about describing images in general and
describing editorial cartoons in specific.
1.3 Purpose of the Study
The purpose of this research was to gather information about user's descriptions of
editorial cartoons. Specifically, it gathered terms and phrases provided by users to describe a set
3
of editorial cartoons, both in an image tagging environment and in a simulated query
environment. The terms and phrases showed what aspects of editorial cartoons are deemed most
typical when describing such images, which in turn suggested both what aspects of editorial
cartoons should be described in large collections and what kinds of detail may be expected by
users. It is hoped that this research will provide a basis for developing further research questions
concerning editorial cartoons in specific and images in general, and will help add to the notion
that there are different kinds of images, and that those different kinds may need tailored methods
of description.
1.4 Research Questions
The specific research questions that will be addressed in this study are:
1.4.1 How are editorial cartoons described in a tagging environment? And how do
those tags fall into Jörgensen’s 12 Classes of image description?
Users were asked to describe recent editorial cartoons by Pulitzer Prize-winning
cartoonists about nationally applicable issues in an online tagging environment. These tags were
be placed into one of Jörgensen’s 12 Classes (1995) except where such placement was not
warranted, in which case new categories were used to group similar tags together. It was not
known beforehand if the tagging activity would yield data as per Panofsky (1939), with basic
descriptions of image composition (pre-iconographic), identification of image components
(iconographic), and description of the cartoon's message (iconologic), nor was it known if Mai’s
theories of domain analysis (2004, 2005) would come to the fore, with subjects tagging cartoons
from the point of view of their individual contexts. Neither was it certain that the administrative,
structural, and descriptive types of depiction that are commonly found in metadata will clearly
manifest in the tags provided by the participants (Caplan, 2003). But these areas, among others,
shed light on how this research can profit practitioners in the field and forward various areas of
image research in academia.
1.4.2 How are editorial cartoons described in a simulated query environment?
And how do those tags fall into Jörgensen’s 12 Classes of image description?
Three weeks after the tagging activity was completed, users were asked to develop search
engine-type queries for the cartoons that they had previously described. It was anticipated that
the tags derived from this simulated query would differ in their proportion in Jörgensen’s 12
Classes than those found in the aforementioned tagging activity because of the change in context.
4
Again, several different points-of-view concerning image description needs, preferences, and
best practices were used to interpret the resulting data with an eye toward both implementation
and research.
1.4.3 How do the tagging terms compare to the querying terms?
As mentioned, it was expected that some sort of variation in the proportion of terms
falling into the 12 Classes (or more, if needed) would differ between the tagging activity and the
simulated query activity. Those differences were examined and scrutinized for what they may
imply, and how that might affect future research.
1.4.4 How might these findings affect the practices of both editorial cartoonists
and image professionals?
After the results from the tagging activity and the simulated query activity were
compiled, they were shared via unstructured confirmatory interviews with both image
professionals and with editorial cartoonists to see if the findings are surprising or expected, and
to gain any other insights that might have arisen. They were given the 12 Classes before the
interview and asked to rank them, then compared the predictions to the outcome of the research.
These interviews are not meant to further inform the research results in the tagging and query
phases of the study, but rather to confirm the validity of the findings by presenting them to
professionals that have some interest in the results.
1.5 Importance of the Study
Although the number of editorial cartoonists employed full-time by newspapers has
decreased (Margulies, 2007), there is still a market for the works of such artists. Amazon.com
lists 46 books about “political cartoons” published just in the last year, and over a thousand total
(2011). Daryl Cagle claims that there are more cartoonists working now than there ever have
been before (Cagle, 2009), and Brooks (2011) continuing series of end-of-the-year compilations
of political cartoons is entering its fourth decade. This indicates an interest in editorial cartoons
generally, and in access to cartoons specifically. This work will benefit editorial cartoonists by
both promoting their works as integral to understanding the world around us, and by helping to
understand how access to past cartoons may be best provided, thus making the images a part of a
more accessible historical record.
Editorial cartoons are often cited in both academic and professional literature as excellent
tools for the classroom when teaching history or social studies. Cagle and Farrington (2009)
5
describes his collection as a “history book” (p. iv) and the cartoons in it “… a thoughtful survey
of our culture, our emotions, our spirit, and our times,” and goes on to claim that cagle.com is
used in classrooms around the country as part of history and current events classes. Heitzman
(1998) notes that cartoons can be effective with younger students where words fail to convey the
gravity of an event. The field of education will benefit from this work by being able to better
access these images to provide color and depth to events long past.
The community of information scientists will also benefit from this work. Research into
how cartoons can and should be described may help to shed light on the concerns of Svenonius
(1994) and Roberts (2001) about the possibility of image description being a worthwhile effort.
It might also help illuminate Mai's (2004, 2005) domain-based opinions in the light of image
description. This research did not confirm the notion that users could not correctly describe the
objects of and actors in editorial cartoons (Bedient and Moore, 1982; Carl, 1968; DeSousa and
Medhurst, 1982), but rather seemed to show that users by and large seem quite able to do so
correctly.
1.6 Scope of the Study
This research drew on academicians in the fields of library and information studies,
history and political science, art history, and journalism. It was assumed that such participants
would have particular insights into how editorial cartoons should be described, as well as the
ability to articulate those insights in the form of tags for each image. The serendipitous
participation of non-targeted audiences was also allowed for, and produced similar results to
those of previously mentioned. The images used in this study were political cartoons from the
following Pulitzer Prize-winning cartoonists: Steve Breen (the 2009 winner), Michael Ramirez
(for 2008), Walt Handelsman (for 2007), Mike Luckovich (for 2006), and Nick Anderson (for
2005). Subjects were asked to comment on these cartoons through the steve.tagger [sic] (2006)
system, a publically-available, open-source image tagger, and which was used for the query
portion of the research as well.
1.7 Definition of Terms
1.7.1 Cartoon vs. comic
The terms “cartoon” and “comic” or “comic strip” mean different things. As per
McCloud (1993), cartooning is a specific artistic style, like impressionism or cubism (p. 30), a
style that most commonly manifests in today’s world in newspapers as single-panel cartoons or
6
comic strips, or in graphic novels such as Maus (Spiegelman, 1986) and Watchmen (Moore &
Gibbons, 2005). The central idea in cartoon art is to emphasize a particular attribute of a
character or item visually through simplistic manifestation, a tradition that draws from the earlier
practice of caricature in the seventeenth and eighteenth centuries. In contrast, a comic has been
defined as, “… juxtaposed pictorial and other images in deliberate sequence, intended to convey
information and/or to produce an aesthetic response in the viewer.” (McCloud, p. 9). Where a
comic stands alone, comic strips use a series of related images called panels in a specific
sequence to relay the passage of time or action.
1.7.2 Editorial vs. political
There is no evidence that either professional cartoonists or academic researchers find any
particular difference between an “editorial cartoon” and a “political cartoon”. There is nothing in
the sparse literature (discussed in Chapter Two) about any name preference for the images in
question. And when one compares the cartoons in Brooks’ Best Editorial Cartoons of the Year
(2011) to those in Cagle and Farrington's (2011) Best Political Cartoons of the Year, one finds an
overlap of authors and, less often, a duplication of the images themselves. It might be argued that
the term “political cartoon” is subordinate to the term “editorial cartoon” because the image in
question may be about a social issue rather than a political one, such as an obituary for a famous
actor or about the Super Bowl, neither of which may be particularly political. Because the
newspaper or other publisher of the image is seeking to comment on a non-political issue, the
best term to apply to such images might be “editorial cartoon,” making the term “political
cartoon” a narrower term in that it refers to a specific type of editorial comment. For this
dissertation, the term “editorial cartoon” was used to speak of cartoon-style visual commentary
on political or social issues, either in single-panel format or in the less often-used strip format.
1.8 Limitations
This research did not deal with the introduction of key phrases and words that did not fall
into the general category of “descriptive,” such as bibliographic information (like “author” or
“URL”), or information that might be present in a record for the sake of the recordkeepers (such
as accesssion numbers or provenance information). Neither Panofsky’s theory of iconology nor
Jörgensen’s 12 Classes allow for a thorough a treatment of such aspects of images as artist, date
of publication, lineage of ownership, and the like, and while their presence will be noted it will
not be the focus of this research.
7
This research dealt exclusively with editorial cartoons that were created and presented as
still images; while the advent of animated editorial cartoons is both noted and lauded, they will
not be the subject of this research. The electronic environment in which this research was
conducted will limit the range and scope of responses to text only; the nuance and depth of
response available through face-to-face interviews was neither available nor sought. The non-
random, purposive assembly of expert users as a participant base precludes both the use of
statistical analysis in describing factors in Class use and the generalizability of the results to the
population at large, as does the blending of less educated collegiate students into the sample.
8
CHAPTER 2
LITERATURE REVIEW
Very little has been written about indexing editorial cartoons. The bulk of the literature
that deals with such images at all comes in two forms: the editorial cartoons surrounding a
specific event and how the course of events may have been altered by the cartoons’ publication,
or those by a given author or artist and how that person altered public opinion to some degree
with the publication of their works. In neither case is the issue of indexing, describing, or even of
just organizing these images for the purposes of either preservation or historical research
addressed, giving way to losing these images by simply forgetting them.
There is literature in the library and information sciences, among others, that do have
some bearing on how editorial cartoons might be gainfully described. Most of the relevant
literature described here is several degrees removed from indexing editorial cartoons but still
contains useful concepts, practices, and standards that can be applied to describing such images.
What follows is a review of the literature that starts with an examination of the state of cartoon
indexing in both the literature and in practice, moving to several theory-based approaches that
point to potential ways of solving the problem, then moving to practical, real-world solutions,
and ending with areas that, at first glance, may seem to have some impact on indexing editorial
cartoons but, for the purposes of this work, will not be used.
2.1 Examination of the problem
Solutions for a problem that has not been thoroughly and properly defined tend to be less-
than-workable and a waste of time and effort. As such, an examination of the state of the art of
cartoon indexing is in order. In this, two main areas of cartoon indexing include: the ability (or
lack thereof) of ordinary people to correctly interpret the intended meaning of an editorial
cartoon, and the state of current cartoon collections and, in particular, how the items in those
collections are described and accessed. Only when these two areas have been plumbed for
relevant ideas and practices can potential solutions come to the fore.
2.1.1 Editorial cartoons: Indexing and Interpretation
While there are scores of works (discussed later) that deal with cartoons in general, there
are few that deal with them as documents worthy of the indexer’s attention or as instruments for
engaging the public. The literature examined here allows us to gain some insight on current
9
practices in indexing editorial cartoons at the newspaper or syndicate level, on the possibilities of
treating such images as we do any other document, on their similarities and differences vis-à-vis
other kinds of images, and on their effectiveness as opinion-shapers in American society. It also
looks into the frequency with which regular users tend to interpret editorial cartoons correctly
(not very often) and the implications this has in trying to index cartoons by subject. What is
avoided here is research that is simply about cartoons, cartoonists, and the place of both in
American history, because these things do not bear on the research a hand.
2.1.1.1 Cartoons themselves Of all the sources cited in this work, the most directly
relevant is Chappel-Sokol’s Indexing Political Cartoons (1996). From this article, we can draw
three basic ideas: editorial cartoons are time sensitive; there is no tradition of describing editorial
cartoons for the Electronic Age to draw on; and editorial cartoons do not exist in a vacuum, but
in a rich and active world that a reader must be familiar with in order to both perceive the visual
part of the cartoon and to conceive the message within it. These concepts, either singly or
grouped together, are echoed elsewhere in this dissertation.
She first notes some of the accolades given editorial cartoons in the past: that they have a
uniquely effective way of dealing with the powerful and privileged, that they can reach an
illiterate audience, and that democracy is all the better because they exist. She goes on to show
that these works are quite prevalent in our society, being found in local newspapers and national
magazines. She then describes the search process of editorial cartoons as follows:
For years researchers have conducted their tedious research by sifting through
piles of yellowed, crumbling newspapers, seeking the page on which the cartoon
was customarily published – never knowing if that particular cartoon was about
the desired subject or by the desired illustrator. (p.22)
She states that most editorial cartoon collections (at least, as of 1996) are mainly for
academics, are entirely cataloged by author/artist and occasionally cataloged by caption. The
large cartoon and newspaper syndicates do not routinely index cartoons at all, citing little
demand for reprints and the costs of doing so, and the effects of time passing on cartoons, stating
that the value of a cartoon diminishes quickly because of these. Chappel-Sokol speculates that
there are three main problems with indexing editorial cartoons at all: indexer bias; choosing what
to catalog, and the potential lack of immediate knowledge of the subject; and that he or she must
have some sort of cultural familiarity in order to “get the joke” and index the cartoon properly.
10
While her view of the state of editorial cartoon indexing is informative, it does not point
the way toward a specific research question; rather, it suggests a research agenda, a set of
questions that would shed light on how best to create a large, searchable collection of editorial
cartoons. Perhaps chief among these is the effect of the passage of time on indexing editorial
cartoons: does this passage actually make indexing these images more difficult? And does
indexing immediately upon publication of the cartoon lead to inaccuracy? Chappel-Sokol also
points out the lack of demand for such services as a factor in indexing efforts, leading one to
speculate about the efficacy of such an effort in the first place. And this article touches briefly on
the introduction of bias into the indexing process, ever present in standard print-based documents
but, it would seem, exacerbated when speaking of editorial cartoons.
When compared to the indexing practices surrounding standard news articles, one might
question the need or the desirability of indexing editorial cartoons specifically, or opinion pieces
in general. While the ability to search for national and local history and events is clearly
something to be pursued, in many cases, newspapers are the de facto repository of local history,
and the prominent national newspapers serve in the same capacity for national events. But can
the same treatment be expected for editorials, either print or graphic? Should these sorts of
documents be preserved along with more traditional news items?
The idea that editorial cartoons are in fact historical documents, ones quite close to the
feeling of the time on a given issue, is found in Weitenkampf (1946), and though the idea is
singular in the literature, it is central to the work being done in this dissertation. He notes that
even obvious partisanship in a cartoon is a commentary on the times and, as such, is a perhaps an
unintentional part of the historical record as well, and that where the creation of standard
paintings or etchings denoting a given event may be years removed from the event that inspired
them, editorial cartoons are, “… a contemporary reaction to events or actions or trends of
thought or prejudices which called forth the caricaturist’s comment” (p. 172). Weitenkampf
contends that editorial cartoons are historical documents in and of themselves, and that while the
use of one cartoon to illustrate one particular issue or event is common, it is possible to use a
series of cartoons over a period of time to get a feeling for public opinion about the event. This
became especially true when the publication of cartoons went from stand-alone broadsheets to
daily newspapers, allowing a greater frequency of cartoons to occur, which in turn expanded the
influence of such images. He concludes that whether a cartoon is a good-natured ribbing of a
11
person or a near-criminal bludgeoning of that persons character, the very nature of editorial
cartoons is that of the historical document, and as such their inclusion in the description of a
given period is both legitimate and warranted.
Weitenkampf’s description of editorial cartoons as historical documents is both accurate
and applicable: accurate in that some historical fact is referred to, and applicable in that this idea
helps us to index such images appropriately, which is to say that an explicit connection to the
event is both possible and necessary. The over-arching idea that Weitenkampf presents is that
editorial cartoons can only be viewed in the context of their times; if one cannot affix a specific
time or event (with its historical connection) to a cartoon, then what might be the most valuable
portion of the cartoon is lost to later viewers and generations.
We might seek information about whether editorial cartoons have been found to have any
similarities or differences when compared to other types of images. Is it wise to expect that users
will treat cartoons in the same way that they treat other kinds of images, or are cartoons a
specific subset of image and thus have a different set of indexing needs? Some direct research
has been done on the topic in question. Landbeck (2002) told six of his research subjects, “I
would like you tell me what you see in this cartoon,” then showed them twenty cartoons chosen
ahead of time. In that work, he hypothesized that subjects would react to the cartoons the way
that the subjects in Jörgensen’s 1996 study did, by identifying the constituent parts of the image
and not the general idea or subject of it. His hypothesis was not supported; the subjects all
responded to the statement by dealing directly with the perceived subject of the cartoon, what the
cartoon was about. Additionally, in a follow-up activity, subjects would group the same 20
cartoons along similar lines, but for different reasons; while subjects would find that the same
four images should be grouped together, they sometimes did so for radically different reasons,
and those reasons were often as mistaken as they were in the first task.
From this we can draw two relevant ideas: that previous research pertaining to image
description by users may not be applicable to editorial cartoons, and that subjects in a study
might not be able to correctly determine the subject of the cartoons, which may in turn skew how
they choose to describe such images. In any case, Landbeck clearly shows that it is possible that
the term “images” may not refer to a generic, one-size-fits-all system of description but that there
may be a need for different systems to represent different subtypes of images.
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Different from the work of Weitenkampf, Brinkman (1968) instead treated editorial
cartoons as opinion changers, as catalysts for change in the minds of readers. Brinkman’s study
looked at editorial cartoons as instruments of either conversion (changing opinion) or reversion
(reinforcing opinion), with 230 subjects looking at two cartoons and two different textual
editorials under varying conditions, and measuring both change and closure. He found that when
presented together, a cartoon and an editorial results in the greatest amount of change of opinion,
that separately editorials are more effective agents of change than are cartoons, and that an
editorial/cartoon couplet where one refutes the other generally results in reversion, while a set
that complements one another results in conversion.
This serves as a counterpoint to Weitenkampf. Brinkman treats editorial cartoons not as
historical documents worthy of collection, organization, and retention, but as substantial,
temporary devices used to help reinforce an already extant opinion. Further, we must consider
the effect of interpretation of editorial cartoons alone compared to that seen when they are
accompanied by supporting text; if we ask a user to interpret a cartoon for the purposes of
description, can we reasonably do so with that cartoon in isolation? And does this hold true in a
Web-based world?
2.1.1.2 Problems in cartoon interpretation There is evidence that naïve users do
not normally interpret editorial cartoons correctly. Most users, when asked to identify the subject
of a cartoon, cannot do so with any degree of accuracy and, to a lesser extent, the same applies to
identifying the actors within a cartoon. But this does not mean that other studies using such users
as subjects are necessarily to be dismissed. If users are asked to describe what they see in a
cartoon, their answers, right or wrong, can be examined as to what kinds of data they are
attempting to describe. Even the inaccuracies themselves show researchers what areas to
concentrate on when trying to describe editorial cartoons, showing what areas need more
attention to detail and accurate, usable answers.
DeSousa and Medhurst (1982) found that there is no evidence at all that “… reliable
claims can be made for the persuasive power of editorial cartoons prior to ascertainment of
reader ability to decode the graphic messages in line with the cartoonist’s intent… editorial
cartoons are a questionable vehicle for editorial persuasion” (p. 43). They describe cartoons as an
inside joke between the cartoonist and the reader, where the image demands a great deal of
political and current event awareness on the part of the reader as well as a good foundation in the
13
allegorical references sometimes found in such cartoons. They asked 130 communications
students to select keywords and phrases from a list for three cartoons – some of which were seen
as legitimate by the researchers, others not – dealing with the three major candidates in the 1980
United States’ presidential election. They found that while most subjects did not use the
inappropriate keywords to describe the cartoons most of the time, neither did they
overwhelmingly choose the appropriate ones; most of the choices that the researchers found to be
correct for describing a cartoon were chosen less than 50% of the time. They concluded that
editorial cartoonists expect a high degree of political awareness on the part of the reader to make
the cartoon work, that there may be a cultural gap between the creators of such cartoons and the
readership, and that it is possible that in an age of television that the skills necessary for the
correct interpretation of such images are lacking in most newspaper’s constituencies today.
This echoes the findings of Landbeck in that both the constituent parts of the cartoons
and the overall point of the cartoon are often misidentified by newspaper readers; while we
might be tempted to assume the subjects for both this and Landbeck’s study misidentified
important aspects of such images because they were university students (as is the case), we must
also allow for the possibility that no group (aside from, perhaps, editorial cartoonists themselves)
will be able to correctly identify the subject of editorial cartoons with any sort of consistency,
which may in turn hobble efforts to have indexers – whether professional or naïve – help
determine either the subject of an editorial cartoon or what elements of such images should be
described in the first place. It also reflects the ideas of Brinkman in that editorial cartoons are
questionable as stand-alone persuaders of public opinion.
Further evidence of the general inability of readers to correctly interpret editorial cartoons
was found by Bedient and Moore (1982). They found that middle and high school students not
only failed to interpret the subject and point of a cartoon correctly, but often had trouble
identifying the actors in such works. Four sets of public school students, in three age groups (131
students total), were given 24 editorial cartoons pertaining to four separate subjects, and the
students’ descriptions of them were compared to those of a panel of expert judges, then
categorized as abstract (correct or incorrect), concrete (correct or incorrect), descriptive, and No
Response. They found that less than one third of the responses were correct overall, although this
varied with age levels and the subject matter of the cartoons. Bedient and Moore concluded that
these results, while not perfectly aligned with those of previous studies, represent similar
14
conclusions: that cartoons are often misinterpreted, that the skills needed for proper
interpretation cannot be seen as a given, and that the teacher in the classroom must teach the
skills necessary to assure that the cartoons will work as educational aids.
This study reinforces the findings of DeSousa and Medhurst: that people (in this case,
public school students) are generally unable to determine the actors or the situation depicted in
an editorial cartoon. While one might point to the methods used to conduct these two studies and
the subject bases drawn from as different enough that the results cannot be compared, it
nevertheless shows that, in general, editorial cartoons are difficult to interpret.
This sentiment was echoed by Carl (1968), who studied adult interpretations of cartoons,
finding that the point the artist intended to make was most often completely different than what
people found in the work. In this study, cartoons were taken from 18 of the largest newspapers in
the country over a nine-week period, and these cartoons were taken door-to-door to ask for
interpretations from a random sample of people in Ithaca and in Candor, New York, and in
Canton, Pennsylvania. Subjects were asked for open-ended interpretations of some cartoons, and
were asked to rank other cartoons that dealt with race relations on a segregation/integration scale,
or partisan politics on a Democratic/Republican scale. The subject’s responses were compared to
the expressed intent of the cartoons, according to the artists themselves. In Candor and Canton
(described as small towns), 70% of all open interpretations were in complete disagreement with
the author’s intents. In Ithaca (the home of Cornell University and, therefore, seen as a more
sophisticated and erudite town), this number was 63%. In all three places, the scaling part of the
study had similar results.
When considering this part of the literature, we can see that the need for accurate and
useful descriptions of editorial cartoons is essential in the construction of large cartoon
collections and subsequently to those collection’s users. We can also see that subjects may be
willing to try to interpret such images and that these attempts can yield useable results. While the
focus of the research at hand does not focus on accuracy or any other such quality, it does seek to
find which aspects of editorial cartoons should be described at all, and it is in this way that the
descriptions given by naïve users – accurate or not – will come into play. When this literature
points out the flaws in user interpretation of cartoons, it nonetheless points to the specific aspects
of cartoons that should be described, and to the larger importance of this work as a contribution
to the history of their readers.
15
2.1.2 Examples of cartoon collections
Examples of editorial cartoon collections abound, but there is a stark contrast between
those that use these images as an attractor or as a pointer to other things and those which have
created a collection of cartoons with an eye toward preservation and dissemination. While there
may be something to learn from the former when comparing it to the latter, it is the latter which
can reasonably be expected to shed some light on how to – and how not – to organize and
describe editorial cartoons in large collections. Such resources include both electronic and print
collections, and show us that while traditional collection-building and presentation methods
leave something to be desired, they also have some solid ideas that should be emulated in future
efforts.
2.1.2.1 Sources In many instances, editorial cartoons are not treated as historical
documents or as relevant to scholarly work. They are instead treated solely as items in that
collection, and as such they are not described with the historian, the anthropologist, or the
educator in mind, resulting in a description of the cartoon that does not treat its historical
underpinnings as an important aspect of the item in the collection. The following examples
demonstrate that some collections can be used as sources for cartoons but are not meant for that
purpose, while other collections are meant to be resources for finding cartoons or for cartoon
research.
Cartoons are occasionally found as items in a large collection or as part of a true archive.
The Claude Pepper Library at Florida State University (2009) lists several specific cartoons in its
searchable database of cataloged items, and many more simply as “cartoons” among the
uncataloged items in the collection. Similarly, the Berryman Family Papers at the Smithsonian’s
Archives of American Art (2009) include “cartoons” as a description of portions of the several
microfilm reels that represent the searchable collection. In both cases, the focus is on managing
the collection, not on providing extensive searchability for purposes other than those concerning
the collection managers and their role in preservation. In a different vein, CNN’s cartoon
“archive” (2009) is not an archive in the historical sense, but is a repository for their editorial
cartoonist’s recent work. Cartoons are listed by author, then by date, with no attention paid to the
subject or any other description of the cartoon aside from providing the captions as a title to the
images. In this, we see that sometimes what is called an “archive” is simply a place to store
16
materials, rather than an organized, purposeful collection of important records, and that there are
archives that serve something other than a legal or management purpose.
In some instances, a cartoon collection is used to point to another resource entirely. The
National Portrait Gallery borrowed several cartoons from the Herbert Block Collection, Prints
and Photographs Division, Library of Congress (2009), to briefly illustrate Herblock’s view of
the presidents from FDR to Clinton. The cartoons available here do not represent the entirety of
the author’s work on these men; in this small collection, most Presidents are examined in three or
four cartoons. While it is possible to get cartoons from this site, it is not possible to examine any
President or presidency in depth, and while it can be used as a source for cartoons, it is not a true
cartoon resource. Similarly, the Smithsonian Institution Libraries American Art
Museum/National Portrait Gallery Library (2009) provides access to portions of some of the
books on cartoons and caricature in its collection. This resource is less of a cartoon archive and
more of a showcase of what is available to researchers in the Smithsonian’s library. For this
limited set of cartoons, access is provided through books by image and by subject, although this
latter method of searching does not seem to be cross-indexed among the images. It seems likely
that the thrust of this effort is to promote the library collections of the Smithsonian and not
provide access to the cartoons themselves. The Pulitzer Prizes (2009) makes the portfolio of
winning editorial cartoonists available online. The cartoons are not described in any way other
than by author and date of publication, thus is not an effort to provide access to editorial
cartoons, but a way of providing insight into what the Committee considers in their deliberations
concerning who should win the Prize. While interesting, it does not provide researchers with a
way of examining political or social issues represented in the cartoons.
In all of these cases, and many more like them, it is not the intent of the respective
entities to provide access to editorial cartoons nor is it their business to determine which of the
cartoons are historical documents and treat them as such. While one can find cartoons in these
places, the cartoons are not the reason they exist. These organizations can be considered sources
for editorial cartoons, but they cannot be considered resources for them. Thus, they should not be
considered when examining ways to index cartoons for research purposes, as they do not intend
their collections to be used for such ends; they are included here to illustrate what a resource (in
the context of this research) is.
17
2.1.2.2 Resources There are several resources – collections that make an effort to
meaningfully organize and fully describe editorial cartoons for future retrieval – that allow
access to the images in the collection only if the researcher is willing to overlook certain
shortcomings in the indexing scheme. The American Association of Editorial Cartoonists (2009)
maintains a web presence for the purposes of promoting both its members’ work and the
profession in general, a by-product of which is a small sample of recent works from AAEC
cartoonists. The cartoons available are kept on the site for one week at most, and are divided into
Local Issues and National/International Issues. This seems to be a resource whose intended
audience is other editorial cartoonists, allowing for reference on both artistic and professional
issues, as well as addressing the needs of educators seeking editorial cartoons dealing with very
recent issues in governments and society.
Mankoff (2004) provides on CD-ROM all 68,647 cartoons published in the New Yorker
from February of 1925 to February 2004. Access is provided to these by using the magazine’s in-
house descriptions, developed ad hoc over a number of years and following no particular system
at all; occasionally, the words within the cartoon or its caption are included in the description,
but this is more the exception than the rule. While the indexing scheme does provide access to
cartoons, the search tool is difficult to use because only one term at a time can be entered, and a
completed search shows all of the terms for that cartoon, including the ill-matched, the irrelevant
and the bizarre.
goComics.com (2009) provides access to 62 editorial cartoonists’ most recent work and,
after free registration, to archives of their work from the time they became a part of Universal
Press Syndicate, which sponsors the site. This access is two-tiered, first by author then by date
within each author. Also within each author are two user-driven avenues for potential
description: the chance to tag each cartoon (which is seldom used), and a chance to contribute to
a discussion board for each image (which, for cartoon research purposes, is used too much). The
result is a community of commenters who seem more intent on keeping a discussion going than
on describing the cartoon for future retrieval. The AAEC’s time-limited collection, the New
Yorker’s ad hoc indexing practices, and goComics’ seldom-used user-based indexing practices
are not fatal flaws for use in research, but are flaws nonetheless, flaws that result from ill-
conceived indexing practices and that seem unlikely to be rethought in the future.
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There are other resources for editorial cartoons where the indexing scheme is well-
conceived but poorly executed. One such resource is the Prints and Photographs Division of the
Library of Congress (2009), which lists the holdings of several cartoon collections. Most of the
images are not available online at this time, but all of the cartoons are cataloged to some degree.
Some records are little more than an artist, a publication date, and how to physically locate the
image, while others offer an inventory of items and words in the cartoon, while still others offer
the context in which the cartoon was created. This variability in the description of cartoons could
be the result of any number of circumstances (manpower, cost, and lack of information among
them), but brings about the overall result of a hit-or-miss system of cartoon description, making
the job of the researcher more difficult.
The Bundled Doonesbury: A Pre-Millennial Anthology (with CD-ROM) (Trudeau, 1998)
is an indexed compilation of all of the Doonesbury comic strips for the first 25 years of its
publication. Indexing has been done by character, date of publication, and subject, and groups of
strips dealing with the same topic provide a timeline. It also provides a list of top headlines from
the week that the strips were published, and trivia lists from those weeks as well, a distraction for
the researcher that does not help provide access to the cartoons in any way. But its emphasis
seems to be more as a way to track characters over time than as a method for recalling
commentary on political events; it’s almost as though the focus of the database is centered on the
phenomenon of the long-running strip rather than on the commentary on the times the strip
covers.
Darryl Cagle’s Professional Cartoonist’s Index (2009) is a good resource for current
American editorial cartoons as well as those from the recent past. It provides access to cartoons
by author, date, and by subject, this latter being a broad-based description of issues pertinent to
the day. Additionally, Mr. Cagle has compiled what he considers the best editorial cartoons of
each year into a permanent, published index, which are also based on subject. One problem is
that the cartoons do not carry with them a date of publication, a major obstacle to researching the
event that inspired the cartoon. Though flawed, the cagle.com site is among the best resources
for finding editorial cartoons. In the Prints and Photographs Division site, the Doonesbury CD,
and the cagle.com site, the intent seems to be to provide access to the respective cartoons in
relevant and meaningful ways, but the result of the indexing efforts can hinder research effort
within these collections because of a lack of granularity and consistency in their description.
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There are a few resources for editorial cartoons that avoid these mistakes, providing equal
coverage to all cartoons, framing the subjects of the cartoons well for the researcher, and
presenting clear descriptions of the cartoons in the collection; these are examples of how cartoon
description should be maintained. The Mandeville Special Collections Library (2009) at the
University of California, San Diego hosts the World War II editorial cartoons of Dr. Theodore
Giesel, better known as Dr. Seuss. The collection represents the entirety of Dr. Seuss’ work
while he was the chief editorial cartoonist for the New York newspaper PM from 1941 to 1943.
The cartoons are presented two different ways, chronologically and by subject, the former a
simple list of cartoons by date and the latter a detailed list of the people, countries, battles, and
political issues examined by Seuss, with cross-references between these four superordinate
headings. These two access points are a good gateway for researchers to search the collection.
Charles Brooks (2009) has compiled what he considered to be the best editorial cartoons
of each year since 1974 and has published them in book form, organized by general issue, topic,
and person. He does not provide a date for the cartoon’s publication (aside from the year
provided in the book’s title), but does provide an introductory paragraph to each subject area,
covering the issue in broad terms and sometimes offering a retrospective evaluation of the public
mood about it. The topics in each year’s section are listed in the table of contents, and the index
provides access to the works of each artist. While this is standard for print works, better access to
editorial cartoons can be offered electronically because of the ability to provide multiple access
points to each cartoon. These yearly books can be used as resources for finding cartoons on
national topics.
The cartoons of the online FDR Archive (Bachorz, 1998) have been provided by Paul
Bachorz’s high school students in 1998. It is among the better-conceived efforts to provide
broad-based access to the editorial cartoons in the collection, providing the typical bibliographic
information as expected, as well as a general breakdown of cartoon topics. Within these, the
representation of the cartoon’s specific subjects varies; where some representations break down
an issue on a month-by-month basis (such as FDR’s attempt to pack the Supreme Court), others
are more narrative in nature and provide brief treatments of the perspective of each cartoon (as
for the Farms Issue) before giving way to a simple list of cartoons. This work recognizes that
while retaining information about the author and publication are important, efforts need to be
made to provide access both by subject and by context for the cartoons in a collection. Although
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the execution of the description may be questionable in some cases, the framework for
description is well-conceived and foreshadows some of the image description metadata schema
that were developed later. In contrast to the sources first listed, these latter resources all attempt
to provide context for the cartoons in their respective collections, treating each image as a
commentary on a given event or trend and providing the circumstances under which the cartoon
can best be understood. In doing this, each of the collections is treating the cartoons as historical
documents, communicative items that can provide insight to a time past or that can act as
qualitative commentary that complements a narrative account of historical events.
The efforts discussed above describing editorial cartoons range from the haphazard to the
well-considered in the way they approach describing the images. Some suffer from problems in
the interface itself, others from the way they seek to describe cartoons. But all of these resources
shed some light on what organizers think their intended audience wants in terms of accessing
editorial cartoons. Both the sources and the resources provided basic, low-level access to images
based on the creator of the cartoon, and generally provide the date the cartoon first appeared in
print, something true even for those collections of cartoons that are best considered sources
rather than resources. Most of the resources described here provide some means of linking the
cartoon to a specific event; some do this through plain statement, others through a paragraph
description, and all do it with varying degrees of success. But it is clear that the people and
organizations that created these collections saw that there is a need to provide access to cartoons
by subject, by the thing that they are meant to describe and comment on. Additionally, the best of
these resources seem to be organized from the top down, on a collection-level basis, which we
might see manifested when users begin describing cartoons during the data collection portion of
this dissertation. In any case, the examples shown here show that it is possible to recommend
methods for describing editorial cartoons, and that there are some common threads found
throughout descriptions of such cartoons, but that the truly well thought-out descriptions take
into account both the collection and the intended audience.
Where the troubles users have in accurately describing editorial cartoons might cast a pall
over efforts to build and describe large cartoon collections, this work instead casts some light on
the situation. While it is true that some efforts to describe cartoons in a collection have yielded
less-than-stellar results, others have indeed shown that collections can be meaningfully and
usefully organized and described; on the one hand, we have some doubts, and on the other, we
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have some hope. We must wonder which will come out ahead at the end of this work: will users
be unable to describe either the subject of a cartoon or consistently describe the same aspects of
those cartoons, as found in the literature? Or will they instead reflect a steady and continuous
approach to describing these images, as found in contemporary practice?
2.2 Conceptual Basis
There are several areas of library and information studies that could influence the
indexing of editorial cartoons. For some, that influence would be minimal because, while
thoroughly thought-out and well-used in other areas, it is simply not applicable to this research at
this time. Such subjects are covered at the end of this literature review, with a cursory
explanation of what they are and why they were not included. But two areas – image indexing,
and the levels of meaning described by Panofsky (1939) and Shatford-Layne (1986) – hold
potential to meaningfully guide and influence this work, and as such are included here as ways to
shape the paradigm through which the work of indexing editorial cartoons will be examined.
2.2.1 Theory: Panofsky, iconography, and Shatford-Layne
Panofsky’s theories (1939) dealt with interpreting the subject of artwork, assuming that
the art in question is Renaissance art; paintings are the main subject that he treats, but his ideas
can also be applied to the sculptures, artifacts, and other art of the era. His theories show that
there is a continuum of meaning that can be broken down into three basic parts: pre-iconography,
iconography in the narrower sense (later called simply “iconography”), and iconography in the
deeper sense (later called “iconology,” to demonstrate the difference between this idea and
iconography). Through each of these, the work in question is interpreted at different levels,
though it may be commonly found that these levels bleed into on another. Panofsky does not
address any area other than Renaissance art, but Shatford-Layne (1986) adds to Panofsky to
produce a broader outlook on image description.
Shatford Layne makes Panofsky’s theories more applicable to images in general – and,
thereby, fitter for use in a library setting – by making the distinction between what a picture is of
and what it is about, the former mapping roughly to pre-iconography, and the latter being split
into a General About and a Specific About, corresponding to iconography and iconology,
respectively. Together, they describe what in an image can be described vis-à-vis the subject of
that image, and they allow for multiple levels of meaning that can be found relevant to different
audiences and different searching intents.
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When dealing with editorial cartoons, the main problem with Panofsky/Shatford-Layne is
that to work at the deepest levels of meaning one must have a good deal of historical background
information at hand in order to understand what is being represented; in Renaissance art, this
would be a background in mythology and religion, and for editorial cartoons it would be a
dynamic awareness of current events. And, as with the metadata schema examined later, how to
properly deal with the words commonly found in editorial cartoons is not adequately addressed
in these theories. Nonetheless, Panofsky’s theories, as modified by Shatford Layne, provide a
solid foundation from which to examine the issues in describing editorial cartoons.
2.2.1.1 Panofsky’s iconology The introduction of his 1939 book spelled out clearly
his notions of pre-iconography, iconography in the narrower sense, and iconography in the
deeper sense, and is the portion of the book most often cited as his definition for these terms,
although incorrectly so. According to Michael Ann Holly’s Panofsky and the Foundations of Art
History (1984):
The three stages are discussed and charted in Panofsky, introduction to Studies in
Iconology, 3-17. This 1939 essay is also reprinted as “Iconography and Iconology:
An Introduction to the Study of Renaissance Art,” in [Panofsky’s book] Meaning in
the Visual Arts, 26-54. It is most interesting to note that between the two publication
dates Panofsky changed “iconographical analysis in the narrower sense” and
“iconographical analysis in the deeper sense,” respectively, to read “iconography”
and “iconology”. (p. 200)
Other works by Panofsky are The Life and Art of Albrecht Dürer (1971), considered the seminal
work on that particular artist; Meaning in the Visual Arts (1955), an update on his more seasoned
notions of iconography and iconology; and Tomb Sculpture (1964), on which he is said to have
remarked that “he had reached an age when it gave him pleasure to be able to look at a tomb
from outside” (Gombrich, 1968). All of his formal works dealt with the methods various artists
used to portray their thoughts through images. This was the primary concern of his academic
career, to elevate the study of meaning in images from an interpretive activity of knowing that a
man, woman, and child in a stable is the Nativity scene to an understanding activity of knowing
what ideas and concepts this particular image embodies.
Panofsky did not initially present these theories to the world as finished product, nor did
he ever name them “Panofsky’s Theory of Art Interpretation” or anything else. In fact, he never
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called them a theory at all; they were simply the stages of art interpretation that he felt were
proper and passed along this knowledge to others. Over time these became more developed and
began to take on the aspect of theory, with Panofsky’s main contribution here to separate the
study of what an artwork is composed of and what it is about.
The pre-iconographic level requires the viewer to identify the basic components of an
image in the most basic and undisputable terms. To illustrate, consider the example of a Nativity
scene. Anyone, regardless of cultural background or knowledge of Christian events or any
demographic factors (minus, perhaps, blindness) can identify in this scenario a man, a woman, a
child, sheep, horses, a cow, and the other figures typically found in a Nativity scene. Neither
particular knowledge of the history of the time nor any training or experience in art interpretation
is required to isolate and name these components of the work. Furthermore, any further
interpretation of the image can only be done after identifying these components; even if we do
this at a glance and subconsciously, we must first perform at this level before proceeding to find
any greater depth of meaning from a deeper examination. On the pre-iconographic level, the
meaning of the scene might be described as a gathering of people, and there can be little debate
as to what is depicted.
Iconography in the narrower sense – generally just called iconography – deals with the
more detailed identification of these components. In the example, the man would be identified as
Joseph, the woman as Mary, and the child as Jesus. No further analysis of the sheep, horses, and
cow are needed because describing them by, say, breed would not bring to light the subject of the
scene in any sense at all; noting that the cow is shown as a Black Angus or a Guernsey would in
no way add to or subtract from the message of the scene. However, noting that the man is Joseph
presupposes that anyone viewing this scene would also know that Joseph is betrothed to Mary,
that Mary is the mother of Jesus, and the circumstances that led to their being with the sheep,
horses, and cow. Iconographic description requires a more in-depth identification of the
components and their relationship to one another in legend or lore is needed as well. At this level
of interpretation, we would say that the scene depicts the Nativity. However, part of the
identification of Joseph comes from the presence of the other components in and the setting of
the image; the man, considered in isolation of the other parts of the scene, would probably not be
identified as Joseph, and the level of interpretation would remain a pre-iconographic “a man”.
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Iconography in the deeper sense, also called iconology, deals with the larger and more
profound meaning of an artwork and moves beyond the components of the work and deals
directly with the meaning of it. Our example of the Nativity scene can be seen to mean the birth
of hope for the children of God, the presence of divinity in a profane world, or a commentary on
the humble beginning of the savior of Man. In any of these cases, a deep understanding of the
historical event shown by a Nativity scene is needed to give meaning to what is shown; a
knowledge of Christian history, the story of Christ’s beginning on Earth, and ultimately His role
in the world is necessary to find meaning on this level. A person who has none of this knowledge
prior to viewing such a work of art would not be able to find any such meaning in it; no intuitive
leap can lead one to view a Nativity scene and identify the work as a representation of God made
human, leaving only a pre-iconographical interpretation possible. Likewise, a person familiar
with Nativity scenes not through a religious background but through American culture (perhaps
having seen them at churches as they passed by them) might be able to identify the people in the
scene more thoroughly but not be able to pull from it the deeper meanings and the significance
assigned to it by practicing Christians. At this level of interpretation, there is room for debate as
to the specific meaning of a work of art, sometimes with completely different meanings and
sometimes with interpretations overlapping.
To put it another way: Panofsky’s iconography is about speaking the language of
Renaissance art, knowing what is meant by each component in a piece. Iconography is concerned
with the icons in art, with the universality of the representation of Love, the Christ, Virtue and
Vice, the Four Seasons, or any other of the ideas or concepts that inspired the art in Europe. It is
akin to heraldry, where one must know what every nuance of an image means, or to the way a
musician knows what tone is meant by all those dots on some lines with squiggles all about (i.e.,
sheet music). If one does not speak the language, one can gather very little meaning from the
conversation.
Iconology, on the other hand, is concerned not with the means but the ends; it deals only
with the sum of the parts and the whole of the work. Iconology deals with the meaning of the
entire work, not the constituent parts of it, and seeks to derive meaning from the whole painting
or sculpture or tapestry. Iconology is concerned with the story being told by the icons, by the
commentary on life being signified by the arrangement and composition of the work in question,
whether it is a warning against vice, praise of nature’s beauty, the folly of fighting one’s fate, or
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the value of family. The biggest difference between iconography and iconology is that former
deals with what is seen, the latter with what is shown.
This can be easily applied to describing editorial cartoons. The need to identify the
constituent parts of a cartoon at the most basic level – the pre-iconographic level – seems to be
the first logical step in deriving its meaning, because without this the image cannot be related to
any news story or current event. Even if we were to suppose that any words were to be found
with a cartoon, it would not necessarily mean that those words would speak to the subject of the
image. But it could, so this must be considered as well.
The iconographic level of interpretation, specifically identifying the actors and symbols
in a cartoon – naming the major things that are seen – would be the next step, and would
probably be more critical to finding the subject or subjects of the image than the pre-
iconographic level. Rarely if ever does a cartoonist depict a political figure or national symbol in
anything like a photographic manner; far more often, the cartoonists employs caricature to show
these actors on the political stage, and the interpretation of those caricatures falls squarely into
the realm of iconography. It is through this action that we begin to approach the subject of the
cartoons, to being able to identify the referents in the image and derive the image’s subject. It is
here that we perceive what is seen.
But it is not until the iconological level of interpretation that we can hope to “get it”.
While we might be able to properly identify all of the constituent parts of a cartoon,
understanding the interplay and positioning and facial expressions of those parts is what leads us
to understand the point the author is trying to make, to go beyond what is seen and understand
what is shown. It is from this level that we can begin to agree or disagree with the point being
made; here is where we discover if the artist has been ultimately successful in communicating his
point, and this is where other literature shows that people most often fail to interpret editorial
cartoons properly.
2.2.1.2 Panofsky and Shatford-Layne Within the broad field of information
science, Panofsky’s ideas have been built upon by the work of the UCLA librarian Sara
Shatford-Layne, who simplified Panofsky’s theories, which made them more applicable to
images in general. Shatford’s adaptation (1986) of Panofsky’s theories makes them more
applicable in the everyday life of librarians and indexers. Noting that the pre-iconographic level
of meaning can be seen as taking an inventory of what is represented in an image or artwork,
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Shatford’s theories duplicate this level of meaning by recording what the picture is Of.
Depending on the expressed or perceived needs and preferences of the users, this level can be
quite basic (to use the Nativity example: a man, a woman, a baby…) or detailed if the user bases
would be, say, other artists (a man in a plain brown robe, a woman with a dull blue headscarf, a
baby in a manger with hay…). At this level, in contrast to the ease with which we can say what
the picture is Of, it is difficult to say what the picture is About.
Not so, says Shatford, with Panofsky’s second level of meaning, iconography. She points
out that since this level of meaning requires a more precise identification of the constituent parts
of the work in question that we must also simultaneously begin to identify the rudimentary
themes and concepts embodied in it. At this level, while we identify Joseph, Mary, and the Infant
Jesus, we also begin to see that this is a representation of the events surrounding the birth of
Christ. We know this because we are – as a culture – familiar with both the composition of such
a scene and with the historical event it represents. Shatford argues that when we properly define
what a picture is specifically Of we also begin, by this same effort, to understand what it is
generally About.
Shatford’s use of Panofsky’s iconology is interesting more in what is does not do than in
what it does. Shatford states that using terms from this level of meaning would be ill-advised
because of the diversity of possible terms that can be applied. As noted previously, a Nativity
scene can mean “the birth of hope” or “the divine made flesh” or any of several other meanings
on the iconological level. As stated by Shatford: “Panofsky’s final level of meaning he calls
‘iconology’; in his categories, pre-iconography is a description, iconography is analysis, and
iconology is interpretation” (p.45). It is exactly these interpretations that Shatford says cannot be
used directly as index terms or for describing the subject of a picture, but they can be used to
give some purpose, motivation, and direction to the terms used in the other two levels of
meaning. By arriving at the conclusion that a Nativity scene means “the divine made flesh” – a
decision based on the needs of our anticipated audience – we focus the terms used in the pre-
iconographical and iconographical levels of meaning, leading to a set of words for describing
this scene that are different than they would be if we concluded that our audience is more apt to
describe the subject as “the birth of hope”.
From this point, Shatford cuts her own path, dealing with the question of specificity;
where books, says Shatford, are usually cataloged under the most specific applicable term,
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pictures might not be because they can refer to a great many more things than does a text.
Shatford cites referential theory, which states that words have senses and references, and
illustrated this by using the phrases “morning star” and “evening star” that have the same
referent but different senses. In this way, says Shatford, “… a word with one sense can have
several referents: the sense of the word “star” could apply to millions of individual stars, all of
them different referents” (p. 46). Shatford asserts that the opposite is true for images:
When one looks at a picture, the process is reversed: the sense of a picture does
not determine the range of its referents, the referents determine the sense of the
picture. In order to apply referential theory to the two-dimensional universe of
pictures and their subjects, referents in this context are defined as the images that
appear in a picture, not as the actual objects or actions that they represent. (p. 46)
Thus, says Shatford, we see that an image is at one and the same time both generic (a man, a
woman, and an infant) and specific (Joseph, Mary, the Infant Jesus) and, depending on the
anticipated audience, both must be accounted for when describing the subject of the image.
It is here that Shatford makes her major change to the theories of Panofsky: she proposes
that instead of pre-iconography there should be the Generic Of (they amount to the same thing);
instead of iconography there should be the Specific Of (which may include some interpretation of
the constituent parts of the image); instead of iconology the should be the About (which deals
with the gestalt of the image). Panofsky progresses in a linear fashion to achieve the
interpretation of an image beginning with a correct assessment of what comprises the images,
then moving to what the parts of the image represent, then basing an analysis of the image’s
meaning on these two levels of interpretation. Shatford states that what the picture is Of affects
the interpretation of what the image is About (as does Panofsky) but allows that the reverse may
also be true, that what a picture is About may have some bearing on what the picture is of,
particularly at the iconological or Specific Of level. Where Panofsky is strictly linear in his
methods, Shatford shows a two-way relationship.
In taking a look at image indexing and the levels of meaning described of Panofsky and
Shatford, we see a great deal of uncertainty, but also a fair amount of optimism. Image indexers
are not certain that a fair and accurate verbal description of an image can occur, while at the
same time the literature shows that the work in this field is progressing and the resulting
descriptions are getting better. Subject analysis shows us that we should represent in proper
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proportion the document, the user, and the domain, but does not reasonably show us what that
proper proportion might be. And Shatford’s additions to Panofsky’s theory show us how we
might describe any image, if we possess the expertise and depth of knowledge to truly
understand what is being shown. In all cases we find that the description of editorial cartoons
will be challenging, but not hopeless.
2.2.2 Image indexing
Image indexing is a well-developed subset of work within the field of library and
information science. It combines the lessons learned about indexing in general and the
organization of both information and information-bearing entities with the emergent knowledge
concerning user behavior with images, and allows us to build systems with at least an idea of
what will be expected by both our intended audience and among image collectors of various
stripes. At the same time, it is an area of information studies that tends to question itself, that
wonders if the effort of describing images with words truly does justice to the former, and that is
ready to acknowledge that, whatever the result when describing images, it will always lack
something which is hard to identify and, therefore, hard to fix.
2.2.2.1 Indexing Considerations The literature surrounding the purpose of indexing
images focuses on the general role of all surrogation: to ensure that a given document (in this
case, an editorial cartoon) can be fully separated from all the other documents in a collection, and
to provide a way for documents sharing certain similarities to be pulled from the collection
together. But when applied to image indexing (among other, specific applications), such roles are
brought into sharper focus and are intended to provide more information and structure than a
simple indexing of text documents. Whether this intention is fulfilled is a matter of some debate,
but that the indexing needs of images for both the user and the system are different than those for
more text-based systems seems clear.
Shatford-Layne (1994) explores what image indexing itself should accomplish, that being
the retrieval of the image from a database of some kind, and grouping like images together.
Classes of image attributes can, she says, be grouped into four broad categories: Biographical,
Subject, Exemplified, and Relationship. Biographical attributes might be further divided to
bibliographic data (who made it, when, how, etc.) and historical data (who owned it, where it’s
been, price, etc.). Exemplified attributes refer to the content of the image in terms of objects; this
deals with what is depicted. Relationship attributes are about what outside objects or works an
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image is related to: i.e., a photo of the Empire State Building while under construction is related
to the Building itself, but a painting of Lee’s surrender at Appomattox is related to a number of
issues in American history. These three attributes are fairly straightforward.
The Subject attribute is more problematic. Various authors in several fields have noted
the there are differences in the way that text and images convey information. This does not mean,
Layne notes, that the differences in subject analysis are completely different also, just that there
may be a different set of considerations specific to images. The first is the difference between Of
and About. An image of the end of a hockey game might be about the Miracle on Ice at the 1980
Winter Olympics. The second consideration is the Specific Of and Generic Of of an image; a
painting might be Generically Of a woman and Specifically Of Whistler’s mother. These subjects
can be further classified in to the facets of Space, Time, Objects, and Activities/Events. The last
consideration is the About aspects of an image, these tending to deal with the more culturally-
based interpretations of a given image. Layne states, “An image may be Specifically Of,
Generically Of, or About any of these facets” (p. 584).
She goes on to say that grouping like images together could be important to a user for any
of three reasons: for the purposes of comparing and contrasting, to allow a user to browse for
unknown content (where she cannot specify what she wants), and to browse for known content
(where she knows what she wants but detailed textual description is inefficient). Another
question to consider is what the groupings are based on. Shatford-Layne directs this question
further by asking if a collection should be based on what is seen by the image as opposed to what
is shown in the image.
Shatford’s four general classes of image attribute can help us to determine if a given
system or schema for image description has covered all the potential aspects of an image, or
whether such a system favors a particular class too much. She shares again her explanation and
expansion of Panofsky’s iconography (discussed earlier), connecting it with her question of why
we group images together, which in turn can help guide the creation of description systems for
editorial cartoons in that it will help us to make consistent decisions where such a question is
important.
Fidel (1997) presents a different conceptual model, one where images can be sought from
what she calls the Object pole, where the image represents what something looks like or as an
example of something (such as a stock photo of a highway), or from the Data pole, where an
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image represents an idea, process, or something beyond that which is inherently included in the
image (such as a map). These poles can be expressed in terms of how the image in question will
be used: if the image is to be used to help illustrate a point in a PowerPoint presentation, then it
is sought from the Objects pole; if the image is to serve as an exemplar when comparing
evidence found at a crime scene, then it is sought from the Data pole. This difference was
derived from an examination of user behavior vis-à-vis image retrieval, and found that the idea
that there is a difference between data-seeking behavior and information-seeking behavior is
transferable to the realm of image searching. Fidel concedes that there are times when images
fall in between the poles, making indexing and retrieval more difficult. As it pertains to database
evaluation, she argues that performance should be measured differently for each of these, as the
needs of one are not analogous to those of the other.
Fidel’s ideas track well with those of Shatford-Layne. Fidel’s Object pole seems to be
speaking to the same general ideas of Shatford’s Of in images, and the Data pole is basically the
same as About. That similar ideas are expressed so differently bodes well for their application
toward editorial cartoons in specific and images in general. The difference between the two
comes in the approach taken to indexing images. Where Shatford focuses on what might be
included in creating a surrogate of an image, Fidel instead is speaking to the thought and
planning that comes before the actual indexing; Fidel is to planning as Shatford is to execution of
that plan. In both cases, recognition of the potential depth and breadth of editorial cartoons is
manifest, and we are better prepared for their description because of these works.
While the foundation of image description efforts may lie in good theoretical footing, the
technical execution that would allow that theory to manifest is at least as important, and that
execution needs to be sound not only within a given collection or institution, but across several
of these organizations, so that all might benefit. Stam’s (1989) research into the history of
computer-based efforts to index large collections of documents found that 1960s American
museums’ efforts “… were characterized by a vision of large-scale, multi-institutional systems
created through concerted effort” (p. 8)., although the purpose of such automation was of
considerable debate because of the lack of cooperative effort among libraries on such things.
From this came a separation of efforts on the 1970s, when formerly cooperative efforts gave way
to individual museums trying to find their own paths to solve their electronic cataloging
problems, but doing so with a custom set of terms for information commonly held to be
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important, which in turn brought about such efforts as the Art and Architecture Thesaurus and
the Museum Documentation Association. At the same time, similar efforts were underway in
most of Western Europe, although with a much more coordinated effort within single nations.
Says Stam: “Thus in the Seventies the quest for a cataloging code to allow universal access to
information about art objects was diverted to concern for gaining control over information within
single projects, institutions, and national units” (p. 13). The 1980s saw the development of
several efforts to integrate (but not necessarily duplicate or emulate) image description efforts
across institutions, works which remain incomplete.
For editorial cartoons, the idea here is that institutions that collect and preserve such
images should make some effort to develop and use systems for cartoon description that would
be flexible enough to meet the needs of each individual organizations but robust enough to allow
interoperability across organizations.
In a similar vein, Trant (1993) observed that a standard language for describing images
had still not come to the fore, despite the development of technologies that would allow diverse
and distant entities to share information about their collections. She first comments on several
separate trends that have to do with images and computers: databases, imagebases, GIS, CAD
and drafting, and several interdisciplinary systems comprise what she calls “a survey of the
history of computers in art” (p. 8). But where interoperability of electronic files had been a
problem in the past, the division between the information gathered and used in such systems
remains. Says Trant:
The researcher wishes to cut across these boundaries, for the works that are
studied as an integral group may be scattered in the public and private collections
around the world. Unfortunately, the very structure of the information itself may
hinder this type of cross-collection searching, precluding the information sharing
that this age of connectivity promises. (p. 9)
To eliminate these boundaries, Trant states that various standards of varying rigor in
indexing have emerged, but finds that these represent solutions to the wrong problems. She finds
that instead of working to standardize the rules and guidelines and definitions, we need to work
toward bridging the gaps between what already exists, essentially calling for crosswalks between
standards, making adherence to rigid rules comparatively trivial while advocating the recording,
in whatever form, of the same information in some way. Thus, where Stam called for standard
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electronic code to allow interoperability across image-collecting organizations, Trant calls for ad
hoc code to allow disparate description standards to work together, thus preserving past efforts
and eliminating the cost of major surrogate replacement efforts while at the same time allowing
for individual collections – and their audiences – to retain the control needed at the local level.
Thus, where Shatford and Fidel seek a universally acceptable way of thinking about the
description of images, Stam and Trant seek ways to make this systemically possible. Among the
four, we find that practice is at least as important as a sound theory itself, and that the solving of
some of the problems surrounding image description in general must be firmly rooted in both. It
may be that, in the description of editorial cartoons specifically, there is an opportunity to
develop a system of description without having to deal with disparate systems or legacy systems;
since there are no well-developed systems of cartoon description, there could be an opportunity
to implement one across several systems concurrently, thus sidestepping some of the technical
issues of Stam and Trant while embracing the ideas of Shatford and Fidel.
2.2.2.2 Concerns Some of the literature centering on image indexing focuses on the
potential pitfalls and other problems inherent in such work. Some question whether efforts in
image indexing can yield results that truly represent the essence of an image in specific, and the
usefulness of such work in general. Others mistrust the environment in which such indexing
takes place, wondering if the bias inherent in indexing on behalf of a collector (either personal or
corporate) skews the representation of images away from what the intended audience would
want. Some researchers find such a disconnect to be a surmountable but often neglected part of
the indexing process, and others find that assessing the audience needs in any way is the major
stumbling block in image indexing.
Svenonius (1994) introduces the twin concepts of the difficulty in indexing images and
the circumstances under which such indexing is best undertaken, along the way suggesting that
there are ways to mitigate these and move the effort of indexing images forward. She starts her
article by noting a particular problem in indexing images: that to describe a non-text item with
text introduces a certain error into the indexing. Her question then is “… is it possible using
words to express the aboutness of a work in a wordless medium?” (p. 600). If there is such
confusion or vagueness about what a subject is, how then can we describe it? Svenonius turns
this on its head by asking if we might first come up with a method of subject indexing, perhaps
later finding the meaning of the subject after this activity. She applies the rules of grammar to the
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problem, with the term “subject” being the thing that is talked about, and “predicate” describing
the thing in some way, acknowledging that this is a simple model and that some form of it could
be implied in any subject indexing method, but then asks if such a model can really be used on
non-textual documents, such as images or music.
Svenonius describes, in brief, that while textual language is largely linear in nature, art
and music are not; words proceed to a conclusion through one proposition building on another,
while art and music are logically different: “… [while] visual forms and musical notes are
capable of articulation, the laws that govern their combinations are different in kind from the
syntax that link words” (p. 601). She notes that while some art is representative of reality (and
should be easily indexable), other art is not, and might lead to problems. She states, “There are
no words for what is expressed. What is expressed cannot be spoken of; it cannot be referred to
using language; it cannot be named and cannot be indexed by index terms” (p.603), leading her
to further state that indexing works best on documentary works like those from Fidel’s Data pole,
those texts that describe a specific set of data and nothing else.
In the realm of indexing editorial cartoons, Svenonius shows us the concept that there is
some loss inherent in the very act of indexing images, and that while this cannot be completely
ameliorated, it can be worked around. She also shares the idea that there are some already extant
systems of organization that can help us to organize images in a collection, in this case sentence
structure. Lastly, Svenonius shows us that indexing works best on those documents that represent
or record something; if we couple this with Weitenkampf’s assertion that editorial cartoons are
historical documents, we are able to sweep aside the gloom shown by other authors and move
forward with the work.
Brilliant (1988) provides a different point of view, questioning the wisdom of having a
slave serve two masters while praising the move of art indices from print to electronic formats.
He notes that art historians must serve dual functions simultaneously and sometimes in
opposition: that of art critic when describing the visual properties of the image, and that of
historian when describing its place in history, thus questioning the circumstances of image
indexing where Svenonius questioned the possibility. Art historians, says Brilliant, “… are
expected to study works of art in a historical context and with a manifest point of view” (p. 120),
but also tend to represent the institution that they are a part of. Once a work is accepted as art, the
art historian then seeks to determine that place of the work in history, answering such questions
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as: how does this serve as an exemplar of a given idea or event?, what does this illustrate?, and
where does this fit in the collection? To this end, he says:
So-called comprehensive indexes [sic], miscellaneous corpora, subject-specific
lexicons, or catalogs… do offer the scholar considerable help in gaining
preliminary access to pertinent objects and to relevant information. Yet their value
is seriously compromised when such publications rely heavily on verbal
descriptions of the artworks and contain few or no pictures. (p. 122)
Noting the expense of such corpora in print form, he praises the advent of electronic versions of
such resources as a means for art historians with fewer available visual resources to do more and
better work when comparing one work of art to another or when placing the object in its
historical place.
In addition to adding Brilliant’s ideas to those of Svenonius concerning the act of image
indexing, he also introduces ideas that have direct bearing on indexing editorial cartoons, namely
the idea that electronic access to such images is far preferred to that provided by print resources.
Along these lines, he echoes the ideas of Mankoff (2004) when he describes the act of putting
over 68,000 cartoons from the New Yorker in to a printed archives as needing to print a book
“… with pages the size of barn doors seemed impractical” (p. 6). Together, this seems to say that
now is the first time in history that large, organized collections of images are possible, thanks to
the development of electronic media and information organization.
Enser (1995) does not question the possibility of image indexing nor the environment in
which it is conducted, but rather he questions the haphazard manner in which it has been
implemented. He notes that over time, societies have chosen to express most of their recorded
information textually, and that this “… amounts to a sacrificing of the message in favour of the
medium” (p. 127), and that as the technology needed to communicate images – first on canvas,
then on film – continued to develop, images were generally discarded as a way to communicate
information of a personal, emotional, or expressive nature. Because of the growth in the amount
of media in general and images in particular, there is now a need for a more inclusive set of
actors on the stage of image indexing, says Enser; since the ubiquity of images extends beyond
the realm of libraries and museums, so too must the pool of ideas extend to other fields of
endeavor, academic and otherwise. While noting that the arrival of inexpensive means of
producing and communicating electronic images has allowed event modest collections to go
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digital, he bemoans the lack of a universal means of describing images, and a similar lack of
translating a user’s visual needs into linguistic queries. And while generally laudatory of
Shatford’s interpretation of Panofsky (discussed later), he finds that, on a practical level, an
indexer must be grounded in the culture and practice that originates the image in order to
properly index it.
Here the lamentation is not for a system of image indexing but for a dramatic increase in
the action of image indexing, under any system at all. The problems brought about by the verbal
description of images in a world previous to large-scale textual printing have been exacerbated
by new technology in both image production and image transmission; likewise, the absence of a
need for uniform image indexing in times past has now given way to a desperate need for such a
thing, editorial cartoons included. It mirrors what was found previously in the review of
electronic resources for editorial cartoons: that no one system has been developed, much less
implemented, that meets the needs of these images in specific or of images in general.
Roberts, an art history professor at Dartmouth, takes a different tack in examining current
efforts to describe images. She found fault with art indexing schemes from before the widespread
use of electronic databases and laments that the opportunity presented by such advances has gone
largely untouched (2001). To date, she says, most collections were organized according to the
basic “bibliographic” data attached to the work of art: artist, materials, date, and so on. In order
to search a collection, one would have to either have previous knowledge of the organization or
use a kind of map, as some collections are organized on a geographic basis. The problem,
Roberts notes, is that art history and criticism has heretofore been excluded from such cataloging
efforts. Some, but not many, critics give a vivid and accurate description of the piece in question,
but there is still something left out. This is the “aura” of a piece of art, says Roberts, is its history,
its context, its place in time. If these things are not known to one viewing the piece, part of the
power of its message is lost. Roberts then asks:
Would it not be an intriguing search if one could find other works of art from
other periods and cultures that advocated the postponement of gratification or
sought promise just over the horizon?... Surely in sophisticated databases some
strategy can be worked out to link the images to bibliographical sources that make
these interpretations, if not fit them into a structured vocabulary, capable of
retrieval. (p. 914)
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Were we to apply this to a system for describing editorial cartoons, we would be required
to note not only the subject or subjects of the image in an historical context but in a philosophical
and psychological one as well. While this might lead to a greater understanding both of such
images and of the politics they describe, one must wonder if such ideas can be reliably found in
editorial cartoons, but the fact that there may be some demand for such things might give us a
clue about how users might describe these cartoons, and where we might focus our indexing
effort.
Enser (2000) sees the state of the electronic art of image description as moving forward,
but not yet fully developed, and ventures a prediction as to where such research and
implementation will go in the future. He states that while content-based image retrieval is
certainly an important place to start when describing images, concept-based image retrieval is
more likely to fulfill the average user’s needs better than the technical information that had
previously held primacy. In this scenario, a user’s linguistic query – sometimes refined with an
authority file such as the AAT or the LCSH – is processed by a text matcher, which compares
salient terms in the query to metadata attached to images in a database. The creation of metadata
that describes meaning and emotion – the concepts – in images is a different activity than that
found when creating it for the content of an image because there is less ambiguity in naming the
objects in an event (both from a factual and linguistic point-of-view), all of which is different
from creating metadata for text, which tends to have linguistic descriptors. To balance these
divergent needs, Enser lauds the advent of what he calls “hybrid image retrieval system[s]”:
At their simplest, such systems enable (i) the query to be posted verbally, (ii) a
text-matching operating to recover images on the basis of content description in
their metadata and (iii) a CBIR technique to accept these images as input to a
similarity matching process which might enhance recall by retrieving further
images without reference to their indexing. (p. 207)
Such a system, combined with relevance feedback from users, is ideally suited to provide
maximum utility for image retrieval, although Enser cautions us not to believe that technology
will solve the problems inherent in the subject indexing of images.
Enser praises the advent of concept-based image retrieval and its ascendancy over
content-based retrieval at this time, declaring that it will be, overall, a boon to the average user;
given the need to connect an editorial cartoon with the event that inspired it, this can guide us in
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our efforts to create accurate and useful cartoon descriptions. One might go so far as to posit that
the description of editorial cartoons could be mostly concept-based, that rather than an emphasis
on the items found within such images, the events and the subsequent emotions seen to be felt by
a society about that event could easily be the most often-cited aspect of it. Certain technical
problems prevent the use of CBIR on editorial cartoons, thus preventing Enser’s notion of a
hybrid image retrieval system from bearing fruit, but the idea should be left open for a time when
such problems can be solved.
Jörgensen (2003a) takes a more pragmatic approach, setting forth what she sees as the
main concerns when developing an image collection and its organization. She avers that there are
five main considerations when indexing images in today’s world: the collection as a unit, the
anticipated user base, the vocabulary to be used, indexing needs, and the context of the image
within the collection. Considering the collection as a unit entails planning out how the collection
itself will be constructed; here, the structure and function of the collection’s description takes
precedence over the content of the system. For editorial cartoons, this could be the collected
cartoons to be published in a given newspaper, or could be the works of a given author; both
would have different requirements for indexing and should be developed independently. User-
centered indexing allows for formal and informal knowledge about the anticipated user base to
come into play when building an image retrieval system; knowing what the cartoon collection
would be used for and whose expectations should be met would help drive and focus the work.
Another matter is the choice of a controlled vocabulary to use in describing the collection: when
used at all, it should represent the concerns of both the users and the administrators. For
cartoons, the differences in the language between a publisher-focused collection and an event-
focused collection can only be guessed, but that there would be a difference is difficult to deny.
Concerns about indexing itself center on the need of the system to provide access to the images
in a collection; the question here is, “what in these images matters to us?,” and for editorial
cartoons, the differences in needs between high school students and academicians would
probably be vast. Context pertains to issues outside the composition of the image which may
alter a user’s perception of it, such as a time period of creation or a particular technique used to
produce it; one can only speculate on the differences between the editorial cartoon indexing
activities on the New York Times and the China Daily. These represent the major concerns of
user-centered design as it pertains to image indexing. They can – and should – serve as a guide to
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the design and implementation of such databases, regardless of the level of expertise in the user-
base, the levels of specificity and exhaustivity required to satisfy those users, or the types of
image contained in the collection.
Ironically, Enser (2008) shows that even with all of the technological and conceptual
progress that’s been made in image retrieval over the last 20 years, there is still a disconnect
between those who index images and those who search for them:
… those involved in the professional practice of visual asset management and…
those at the cutting edge of research in image retrieval… need a shared perception
of the principles and practices that guide their respective endeavors if both
opportunity and challenge are to be addressed effectively…. Sadly, it remains the
case that professional practitioners have only a minimal engagement with the
activities of those occupied in image retrieval research, and the endeavors of the
latter community have been little informed by the needs of real users or the
logistics of managing large scale image collections. (p. 3)
When considering the potential disconnect between the reader of editorial cartoons and those
charged with maintaining a collection of them, it would seem that technology could provide a
bridge between the two, although, as noted for goComics.com (2009), the effort has not borne
fruit to date.
As with image indexing, a number of similar problems with the idea of subject analysis
have been examined and explained in the literature. Hickey (1976) points out dual problems with
the basic act of indexing. One is the duality present in the task of indexing, that we are making
the document unique but the content interconnected. The other is that while we practice subject
indexing, we have no concrete definition of what “subject” means, leading to a turn toward
centralized authorities that do not keep up with changes in indexing practices or needs. He then
moves on to describe a brief history of cataloging in America, particularly the move from Dewey
to LC due to economic concerns and the curious lack of interest in American libraries in the
theories of classification through the years. Hickey goes on to describe the problems discovered
once LC had become the dominant system in libraries (difficulty updating and lack of
consistency being chief among them).
Where Hickey points out macro-level problems in subject indexing, Blair (1986) draws
attention to problems at the micro-level. He posits that there are two kinds of indeterminacy
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when accessing documents by subject: inter-indexer, where several indexers do not consistently
apply the same terms to similar documents; and term selection, where we are unsure of which
terms a user will use to construct a query. He finds that in most cases a successful query can be
made, even with these problems of indeterminacy confounding the query. He suggests that the
more familiar the user is with both the system itself and with the domain the indexing covers, the
more likely a successful query will result. Blair then suggests that the best queries can be built by
using a seed document, and exemplar of what is desired by the user, and that a system should be
built to retrieve similar documents to this document to facilitate searching the system.
Brookes (1980) takes a different approach to criticizing subject analysis practices: instead
of attacking system- or indexer-level practices, he questions the entire effort. He uses Popper’s
worlds to illustrate his vision for information science as a legitimate field of work: World 1: the
physical world, and everything in it; World 2: world of subjective human knowledge, or “mental
states”; World 3: objective knowledge, recorded products of the human mind, artifacts.
Information science should contribute to the world organizing World 3, describing what there is
in World 2 accurately and systematically, turning documents into knowledge. Brookes argues
that the complete contextual information in World 2 cannot ever be fully described for two
reasons: one, measuring such things can change the thing measured, and two, some factors in the
“mental state” of information formulation and processing will not and cannot be known to one
recording the information. Brookes echoes the thoughts of Svenonius and Roberts here in
despairing of the possibility of accurately describing the subject of any image at all. Here it is not
the wonderment of Svenonius or the lamentation of previous practices by Roberts but the simple
statement of fact that all of the factors which go into the concepts and ideas a person might have
cannot be perceived outside that person’s mind. This leads us to search for patterns of
incompleteness in any given subject analysis, for loose ends of ideas that might be partially
described but left mournfully incomplete.
Bates (1998) points out that “the user’s experience is phenomenologically different than
the indexer’s experience” (p. 1186) because the indexer has an item to examine where the user
only has a need. In addition, the indexer also has, by dint of association, far more knowledge
about the indexing system, anticipated user base, the other works in the system, and the intent of
the system than does the user, further removing the understanding of the indexer from that of the
user. Bates further points out that while we might understand how users would come up with a
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variety of terms for the same topic, it is harder to understand why the indexer would do the same
thing. As we progress in expanding our considerations when analyzing the subject of a thing that
pitfalls still exist, these considerations must be remembered.
This sentiment is echoed by Swift, Winn, and Bramer (1977) when they lament the fact
that traditional indexing activity assumes that what a document is “about” forms the basis of both
description and of searching, since it means that the indexer can readily discern what a document
is in fact about and that the searcher has a clear idea in mind about what is being sought. Often,
they find, the searcher and the indexer use the term “about” in different ways, resulting in a
dissonance between the results of both party’s labor. It is then posited that a multi-modal search
should be enabled, one in which the various aspects of a given document are described, such as
theoretical orientation, methodology, and so on, so that not only what a document is “about” is
covered, but how it came to be about anything at all is also recorded.
Finally, Hjørland (2001) states that the aboutness of the subject shares most of its
theoretical underpinnings with the ideas of subject, topic, field, discipline, and such, and that
these ideas are separated by the needs of the indexer and the situation that a document is indexed
in. He states flatly that “subject” and “aboutness” are one and the same, and that one cannot
define either without a host of other, pre-defined terms, such as topic, theme, domain, and field,
among others. He also finds that other concerns come into play: professional consensus and
theoretical conjecture certainly influence both how we define terms and how we use them in the
real world. He states that even if we were to agree on the definitions of subject and aboutness
and other related terms, relevance is another matter. He states that it is possible for two
documents to be about the same subject, yet one will be relevant and the other not, and that
relevance, like subject description, necessarily passes through many hands before a final verdict
is found.
One must question the woe-is-me attitude found in some of these works. While it is
reasonable to assume that the representation of an image in words will necessarily mean the loss
of some meaning or intent or message, the speculation that doing so might not be worthwhile at
all is ludicrous; all representation is lossy, from the table of contents to the index in a book, to
the cards in a catalog, to the abstract for this dissertation, all reduction of any message means
losing some of that message. It is known, it is expected, and the reiteration of the fact where
images – with the implicit expectation that somehow, this time, it should be overcome – is
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unreasonable. Nonetheless, it is a concern which should be addressed: in the reduction of an
image to a textual description, attention should be paid to what is included and how, and the loss
of information in such a conversion should be carefully managed and purposefully undertaken,
with the wants and needs of potential users taking primacy over other considerations..
2.2.2.3 User Behavior Counter to these lamentations is research that describes how
users go about describing images, what terms they use when searching for images, and how
those terms might be grouped and described. The research presented here seems to occasionally
be at odds with itself; to date, it has not supported conclusions about universal categories of user
descriptions, the nature of user queries for images, or the focus of those queries. Nonetheless, the
findings of other researchers in these areas at the very least raises our awareness of them in this
research, so that we might move forward cognizant of the potential problems we might face in
both data collection and data interpretation.
Studies of user’s queries for images have resulted in some general conclusions about
what users look for when seeking images, but nothing specific has been found across the
research. Armitage and Enser (1997) found that users seeking images in a library databases
asked more often for people and places in the specific, and far less so for people and events in
the generic. They found that this was true across different types of libraries (to varying degrees),
but that in all cases the request for images that display abstract concepts was minimal to non-
existent. The researchers collected their data from seven different libraries in Great Britain (two
motion picture archives and five still image archives), whose respective staffs collected a total of
1,749 image queries that each staff found typical of the requests made of them. An analysis of
these queries using the researcher’s Panofsky/Shatford mode/facet matrix found that the most
often sought images were of specific people, specific places, and generic people; that requests for
specific things outnumbered those for generic things; and that both of these vastly outnumbered
requests for abstract things. Aside from their obvious endorsement of Panofsky and Shatford,
Armitage and Enser speak to the level of specificity that should be sought in an image database:
we should expect that users will seek and describe specific items within an editorial cartoon or,
more likely, will seek cartoons about a specific event, such as the Iraq war, rather than cartoons
about war in general. In doing this, Armitage and Enser looked at the image query behavior of
users of all levels of expertise.
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Taking a different slant, Hastings (1995) examined queries by professionals in the field of
art history. She found that queries by art historians were of variable complexity, the simplest
having to do with what might be called bibliographic data (artist, medium, etc.) and the most
complex dealing either with references to secondary materials to give a full and robust history of
the image, or to place the image with others based on artistic schools-of-thought or historical
eras. She found that, within the art historical research field, that there can be four levels of query,
the first representing direct questions with simple answers, such as the name of the artist or when
the image was created. The second level represented either comparisons made by inquiry (“Did
the same artist paint both of these pictures?”) or sought more and different textual information
that that already provided with an image. Hastings’ third level of inquiry comprises questions
that sought to identify the object, actors, or actions within an image, and her fourth level
represents the most complex questions, such as “what does this image mean?”. Again, we see an
implicit endorsement of the Panofsky/Shatford point of view: Hastings’ third level of inquiry
matches well with Panofsky’s iconographical level and Shatford’s idea of Specific Ofness, while
the fourth level corresponds to Panofsky’s iconological level and Shatford’s idea of Aboutness.
But in a broader context, Hastings showed that even within a relatively homogeneous group of
users, queries cover a wide range of wants and needs.
Where Armitage and Enser examined image seeking within a specific set of catalogs, and
where Hastings restricted her research to art historians, Goodrum and Spink (2001) studied
image seeking in a popular search engine. They found that users generally used few terms to
search for images on the Web, and that these same users reformulated their queries only a few
times. They speculated that at least part of the problem for users when searching for images is
that there might be a disconnect between the words that describe the information need and those
that describe the image being sought. They arrived at this after analyzing over 35,000 image
queries on the Excite search engine, finding that most queries had more than three terms and that
users often had more than three queries per session. They concluded that more research needs to
be done to find out how users represent their needs when formulating image queries, and that the
representation of higher-order items (those beyond CBIR) need more scrutiny. We see here,
again, a call for description beyond the simple and unarguable; this research supports the need
for iconological description as a necessary component in any descriptive system. Additionally,
we might hope that a well thought-out metadata schema would help to alleviate problems in
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query formulation for the user by providing insight into what categories are present and actively
recorded.
In all three studies, we see that there are some problems matching user behavior when
searching for images to the descriptions provided by information organizations. In most cases,
we see that users search for objects within an image, represented in concrete terms that allow for
little variation. We must wonder if the more abstract aspects of images can be represented based
on some sort of system, and if we can systematically determine the nature of the terms in such a
system. In any case, it seems that current image description systems cater to the most basic needs
of those seeking images, and this is a foundation on which better systems of description can be
built.
Approaching the problem from another angle, some research concentrates on the ways
that users describe images as opposed to searching for them. In doing this, a dichotomy is found
between the search for and the description of images. Using similar methods but getting different
results from Jörgensen, Greisdorf and O’Connor (2002) found that users place a premium on
naming the objects in an image for use as search terms, and that the emotional impact or effect of
an image is an often-sought aspect. They proceeded from the assumption that “… viewer’s
percepts generally fall into image attributes that can be described as color, shape, texture, object,
location, action, and/or affect” (p. 11). The research represented in this article seeks to confirm
these seven categories as those commonly used by users to describe images, in settings with and
without pre-selected word lists. The researchers found that users described the content of a
picture only when presented with a list of potential descriptors, not when allowed to freely form
descriptions; that CBIR-based retrieval methods are largely inadequate to user’s retrieval needs;
and that there is often a gap between what is pictured in an image and what users perceive the
image to mean or to be about. This would seem to be at odds with Jörgensen’s findings (1998) in
that, when given similar tasks, these researchers found that affective traits were those most
sought where she did not. In any case, Enser illustrates the need for accurate and useful
descriptions of context-based items of interest.
Hollink, Schreiber, Wielinga, and Worring (2004) worked along similar lines as
Jörgensen (1998) and Armitage and Enser (1997), differing from the former in that they found a
greater use of abstract descriptions, and from the latter in that the general level of description
was used more often than the specific. Nevertheless, they found, as did the aforementioned
44
researchers, that the objects in an image are the things most often used both to search for and to
describe that image. They unified the elements of several image description systems, creating
three general categories of descriptive elements: non-visual, perceptual, and conceptual. They
also developed a system for describing potential users of image retrieval systems as well, one
that takes into account the domain, expertise, and task of the user. They also found that
conceptual elements were used far more often than the other two elements, that within the
conceptual level there were far more instances of object descriptors than all other descriptors put
together, and that the use of descriptors varied widely between the describing and querying tasks.
That abstract concepts are used more often than are specific objects is in direct opposition to the
findings of Armitage and Enser, a state of affairs that casts doubt onto what might be found when
editorial cartoons are the images used in such tasks. And the findings that conceptual attributes
of an image were used more often than the non-visual and the perceptual seem to be counter to
the findings of Jörgensen, who found that literal objects were most often sought and described by
users.
As a whole, this research supports several conclusions. It shows that descriptions from
users can in fact be analyzed and that categories of description will be found, but it does not tell
us with any certainty what those categories might be. The research also shows that while objects
will probably be a portion of what users describe, the subject of the editorial cartoons may not be
described unless users are prompted to do so. It shows that there may be some predictive factors
in the level of expertise in image interpretation that will affect the type and depth of a query for
editorial cartoons. Most of all, the research shown here supports the idea that meaningful data
can be derived from an examination of user descriptions of images, and that the data can guide us
in the creation of image description systems.
2.2.2.4 Domain-based approach Relatively new to the arguments over proper
indexing of both images and textual works is the idea of the domain in which the entity may be
said to belong. In domain-based indexing, while the document itself remains an essential part of
the representation equation, the domain – the subject area, the field of study – that the
document’s author comes from is considered as well, as it is assumed that this domain will help
set the stage for what the document has to say. Domain analysis is used to solve some of the
problems found in traditional indexing practices, such as which authority file to use or what the
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primary subject of the document may be, and when used together can produce a better, more
accurate, and more useful surrogate for any document.
Possibly the foremost champion of domain analysis over the last ten years is Jens-Erik
Mai. In 2004, Mai states that traditional classification is an effort to represent reality, to describe
a relationship between manifest concepts as found in the world we live in. To say that one
system is more representative of reality than another would be a difficult argument to make since
knowledge structures are centered on the individual, making for multiple realities and, therefore,
the need for multiple systems of classification. The article also describes the logic of
classification, specifically the ideas of exclusivity and exhaustivity, ideas that presuppose that
bibliographic classification takes place along the same line as those observed in nature, that there
is a natural order to things which can be used to order the world of books and other documents, a
notion Mai dismisses because the various living things of the world are classified by their
physical characteristics and (picking up on a familiar theme) defines only groups, not individuals
in that group. What, then, is the connection between scientific and bibliographic classification
philosophies and practices? Mai introduces the idea that words are constantly dynamic, always
changing (perhaps subtly, perhaps not) in meaning. He argues that, like scientific inquiry, all
language and knowledge must necessarily draw on previous language and ideas, and since these
change over time, language is fluid.
Mai’s 2005 work is perhaps his most forceful and convincing argument for the
importance of domain analysis in subject indexing. He finds fault with the traditional practice of
using the document as the unit of analysis when indexing a document of any type, including
images, something he calls the document-centered approach. A variant on this is the document-
oriented approach, which does the same thing but allows for the consideration of questions
which might be asked of the retrieval system the document resides in. Both cases, says Mai,
“…assume that the subject matter of a document can be determined independently of any
particular context or use” (p. 600). This gives way to the consideration of the context of the
document, both within a given field and for a given individual:
A reader does not respond to the meaning of a text. The reader’s response is the
meaning of the text… Language belongs to the community in which it is used. It
is the community and its activities that defines and determines the meaning of the
words used. (p. 604, emphasis in original)
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Building on the work of Hjørland and Albrechtsen, Mai describes a domain as a like-minded and
-intended community represented through documents of some kind. Domain-centered indexing,
then, starts with an analysis of the domain and works its way toward the document in question,
seeking to properly place the document within the domain. Mai concludes that the use of
domain-centered indexing will allow for a more accurate representation of a document’s
relevance in a given collection, in the field, and in response to user’s queries.
Hjørland (2002) offers a survey of 11 approaches of domain analysis: producing literature
guides, constructing thesauri, indexing specialties, empirical user studies, bibliometrics,
historical studies, genre studies, critical and epistemological studies, discourse studies (both on
the small and large scales), structures in scientific communications, and artificial intelligence or
expert systems. He describes these approaches as being practices found in information science
and as largely independent from library science, and as related to similar practices found in
computer science. He also avers that there are several examples in both theory and practice
where two or more of these approaches overlap, leading to greater discovery and understanding
of domains both within and without.
Anderson and Pérez-Carballo (2001) note in a survey of indexing criticism that the trend
in indexing practice and is to merge previously separate indexing factors, notably the presence or
absence of vocabulary control, the exhaustivity of indexing, and what qualifies as indexable
material. They also echo the thoughts of others when they lament the vague guidelines provide
by textbooks and other forms of instruction dealing with the practice of indexing at the
individual level, rendering the basic instructions for indexers as: perceive the text, interpret the
text, then describe he text as it would fit in a particular system for a particular audience. They
find that “… the one thing we definitely do know about human indexers is that they rarely agree
on what is important in a message, or what to call it” (p. 243). In contrast to the mental process
of the individual indexer, Anderson and Pérez-Carballo then allow for Fugmann’s argument that
it is not the place of indexing to deal with the individual but rather with the social aspects of
description, shifting from rule discovery to rule construction.
Most of this speaks more to information professionals than to naïve users. A basic
philosophy can be gleaned from the literature, and when coupled with the demands of a given
work environment can guide the professional in theory and practice for a particular collection.
The literature deals less with how non-professionals might deal with similar work. While we can
47
imagine that there are certain aspects of subject description in editorial cartoons that might
manifest regardless of the indexer’s experience or training, we must ask what descriptions fall
outside of the guidelines offered here.
2.2.2.5 Jörgensen’s 12 Classes Jörgensen’s 12 Classes have been used as a basis for
comparison, as a starting point for the development of further Classes, and as an example of real-
world scenarios from which image descriptions can be derived. Her research in this area is
heavily cited, well thought of, and is sometimes used as the basis for other researcher’s efforts in
image description. It is the foundation of the research at hand, as both the techniques shown by
Jörgensen and her results form both the focus of these efforts and the standard against which they
will ultimately be measured.
2.2.2.5.1 The Classes. Jörgensen (1995) found that freely given descriptions of
images, and an analysis of queries for images, can be parsed into 12 Classes of image
description: LITERAL OBJECT, COLOR, PEOPLE, LOCATION, CONTENT/STORY, VISUAL ELEMENTS,
DESCRIPTION, PEOPLE QUALITIES, ART HISTORICAL INFORMATION, PERSONAL REACTION,
EXTERNAL RELATION, and ABSTRACT CONCEPTS. She also found that the frequency of any given
Class was at least partially dependent on whether the person was describing the image, or
searching for it. Jörgensen found that when describing an image, the most common Class of
descriptor was LITERAL OBJECTS (used 34.3% of the time), followed by COLOR (9.2%), PEOPLE
(8.7%), and LOCATION (8.3%), but that when the description was derived from a query, LITERAL
OBJECTS were used 27.4% of the time, followed instead by CONTENT/STORY (10.8%), LOCATION
(10.7%), and PEOPLE (10.3%).
In this research, Jörgensen contributed two ideas that serve as the foundation of the
research in this dissertation. First, she provided a platform for comparison when analyzing other
descriptions of images; in providing the 12 Classes, other researchers can now build upon them,
expanding them if necessary, and comparing future results to those she and others found.
Second, she showed that the description of an image in at least in part dependent on the context
in or purpose for which the image is being described. Future researchers can build on this by
both repeating the two main settings she used to produce the results, and exploring other possible
image description needs that can in turn be used to provide better access to large image
collections.
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Jörgensen (1996) states the problem of image indexing as one driven by the fact the
indexers have little idea what terms patrons might use to find images, this ability being a new
one for most of the user community. Previous research found that user descriptions fall into one
of three broad ranges: Perceptual (factual description of the image: color, objects, etc.),
Interpretive (from having some knowledge of what the Perceptual attributes mean), and Reactive
(purely internal and specific to each test subject; “liking” the image, for instance). Within these,
she found that her 1995 research helped to further divide image description into more useable
categories than just these three.
Jörgensen then used the same images used previously with a new group of people who
were then asked to describe the images in terms of what they “notice,” just as before. However,
this time the participants were given the Classes in a template, and asked to “slot” their terms as
they saw fit. The Classes were listed in random order for each subject to ensure against bias. The
results showed that the use of the template did change the frequency with which the terms were
used to describe the images. While the LITERAL OBJECT was still the most used, it was only used
17.7% of the time, followed by CONTENT/STORY at 14.9%, up from 7.4% in the previous study.
The results this time showed a much more even distribution in terms and a very different order of
which Classes were used most often. Additionally, the random order of the Classes on the
template sheet showed no significant difference in frequency of use.
The key concept to be gleaned here is that there is a clear difference between the thoughts
and terms that are used when describing images freely and when those same images are
described within a description system. While Jörgensen’s 12 Classes do not constitute a metadata
schema – there were no rules for application given to the test subjects, among other issues – it
does serve the purpose of illustrating differences in behavior between the indexer and the user;
both give reasonable descriptions of the images in question, but these descriptions are different
as a matter of course, which must be taken into account both when creating and when evaluating
such systems.
Jörgensen later (1998) found that, among naïve users, image descriptions would consist
mainly of four general attributes: OBJECTS, PEOPLE, COLOR, and LOCATIONS, with
CONTENT/STORY sometimes needing some consideration. Further, she found that maintaining a
high number of attributes within these classes allows for the systematic description of a wide
variety of images across a wide range of users. Jörgensen collected data from three different
49
tasks: describing known images (where the subject could see the image being described),
describing theoretical images (where the user envisioned images and then described them), and
remembering images previously seen. The descriptions were then categorized according to
attributes, which were subsequently categorized into the Classes that she had derived from
previous study (Jörgensen, 1996). She found that while there were variations in the rate of
occurrence for each class of attribute across the three tasks, they all followed similar patterns
based around the classes previously discussed, although the attributes derived from the subjects
describing images from memory showed the greatest variation. Thus, where Hastings and
Armitage & Enser found particular categories in user’s terms when they search for images,
Jörgensen found that different kinds of terms were used when users describe images, showing
that the tasks may be different and thus opening an avenue for research into the difference
between these activities and the implications for image retrieval.
2.2.2.5.2 Description. Several studies have based the analysis of image description on
Jörgensen’s 12 Classes, using different kinds of images and comparing the frequency of use for
each of the Classes to those found by her. In 2002, Brunskill and Jörgensen applied the 12
Classes to various data-related images, such as weather maps, baseball stadium seating charts,
and the internal anatomy of an elephant. The most noted Classes of image description in that
case were LITERAL OBJECT, CONTENT/STORY, and ART HISTORICAL INFORMATION. It should be
noted that one of the results of this particular study was a customized set of classes for use
specifically with those types of images, and that the frequency of use shown in Table 1 is
different than that shown in the 2002 study because of the need for commonality of definitions
within the research being presented in this dissertation.
Laine-Hernandez & Westman (2006) applied the 12 Classes to newspaper photographs in
an effort to see if those Classes could be used as the basis of a descriptive system of those
images. Where Jörgensen’s four most used Classes in the descriptive activity were LITERAL
OBJECT, COLOR, PEOPLE, and LOCATION, Laine-Hernandez & Westman found that their scenario
had LITERAL OBJECTS, followed by CONTENT/STORY, DESCRIPTION, and LOCATION as the most
often used Classes, showing a change from the previously conducted research.
In both of these cases, the frequency of each of the 12 Classes seemed to depend on the
kind of image being dealt with; as the image type changed, so too did the most-used Classes of
description. See Table 1 for a breakdown of Class usage in these studies. It can then be expected
50
that yet another set of frequencies might be generated when the images in question are editorial
cartoons. If this is not so, it might then be possible to say that editorial cartoons can be indexed
in the same way that the most closely mirrored type of image is described. Table 1 summarizes
the findings across all three image sets described here, and across all 12 of Jörgensen’s Classes.
Table 1
Summary of frequencies for Jörgensen’s 12 Classes across three sets of image – tagging
environment
Jörgensen (1996) Brunskill & Jörgensen (2002)
a
Laine-Hernandez & Westman (2007)
The 12 Classes Illustrations Scientific images Newspaper images
Literal Object 29.3 24.2 29.1 Color 9.3 7.4 6.2 People 10.0 0.3 7.0 Location 8.9 1.3 10.2 Content/Story 9.2 24.1 17.4 Visual Elements 7.2 5.3 4.0 Description 8.0 4.1 12.0 People Qualities 3.9 0.3 8.7 Art Historical Info 5.7 12.7 0 Personal Reaction 2.9 6.3 3.6 External Relation 3.7 10.9 0.3 Abstract Concepts 2.0 3.1 1.7
a Jörgensen & Brunskill data is different than that previously published because previous definitions were different than those used here.
2.2.2.5.3 Queries. It has been noted that data gleaned from a query environment will
yield different results than that found in a tagging environment. Jansen (2007) parsed the queries
of 587 images searches on Excite.com into the 12 Classes, comparing those findings to those of
Armitage & Enser and of Chen. He found that in those web searches, the four Classes used most
often to retrieve images were LITERAL OBJECT, CONTENT/STORY, LOCATION, and PEOPLE. He
also noted that providing for three additional classes external to image content – URL, COST, and
COLLECTIONS – allowed for certain Web-based information to be tallied as well, providing a
somewhat different set of frequencies in the data.
Chen (2000), in a similar vein, analyzed the queries of 29 art history students who were
required to find at least 20 images as part of an assignment. He used three raters to determine
where each of the discrete descriptions should be placed within the 12 Classes, and that
LOCATION was the Class most often used (although Chen used a modified definition of it),
followed by LITERAL OBJECT, ART HISTORICAL INFORMATION, and PEOPLE, producing yet
51
another set of main descriptions within a given image set. He suggests that there may be some
impetus to drop some of the seldom-used Classes, and to divide to overly-inclusive ones,
particularly LITERAL OBJECT.
In both cases, a divergence of frequencies is again seen in the query activities of the
researchers, producing differing results. LOCATION, PEOPLE, and LITERAL OBJECT continued to
be the most used Classes, just as in the description scenarios, suggesting that these may be some
sort of super-Classes, where a high frequency of occurrence is found across image types and
activities. See Table 2 for a comparison of the query analysis results.
Table 2
Summary of frequencies for Jörgensen’s 12 Classes across three sets of images – query
environment Jörgensen (1996) Jansen (2007)
a Chen (2000)
b
The 12 Classes Illustrations Excite.com Art history students
Literal Object 27.4 21.7 25.4 Color 9.7 1.0 0.5 People 10.3 30.2 10.8 Location 10.7 4.0 32.6 Content/Story 10.8 0.3 1.0 Visual Elements 5.4 0.8 2.3 Description 9.0 30.5 1.1 People Qualities 3.9 3.4 8.4 Art Historical Info 5.7 0 12.8 Personal Reaction 1.9 0.2 0 External Relation 3.8 0 0.8 Abstract Concepts 1.5 7.9 4.4
a The percentages for Jansen were re-calculated to exclude three additional Classes that resulted from the research: Cost, URL, and Collection. b The percentage for Chen were recalculated to show the total percentage of each class that was agreed upon by at least two out of three coders.
2.3 Practical Applications
Where the conceptual basis for going forward with this work may leave us with some
uncertainty as to what should be done, there are some practical, real-world efforts that use
advances in communication technology to allow and encourage a far broader range of
participants in the indexing process than had been seen before. The use of metadata allows
interested but non-professional users of a system to both provide data in a system and to help
form the elements of that system. Folksonomies allow quick and easy provision of data to
interested parties without fear of reprisal or ridicule, and without recompense. In both cases, end
users – untrained, with a range of interests, and unrestrained by a strict system of description –
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can now provide insight into how that system of description should be built, and into how it
should go about describing the items in a collection.
2.3.1 Metadata
In its broadest sense, metadata is data about data, but this definition tends to disappoint
when discussing the thing or when implementing it in the real world, because such discussions or
implementations tend to be focused on particular aspects of metadata to the exclusion of others.
As an example, at the most basic level, “10” could be an example of metadata, in the sense that,
standing alone, it represents some aspect of a document that has been deemed worthy of
description. More inclusively, metadata is sometimes meant to include an element or tag to be
used in combination with a value; in this example, we might find the header “number of pages”
is coupled with “10,” providing more meaning than the previous example did. In some instances,
a reference to metadata is a reference to a set of tags, known as a schema, which adds to the
comprehensiveness of word. And in other instances metadata refers to several schema at once, or
to the entire field of study.
When discussing metadata, it may be fruitful to establish at the outset exactly what
“metadata” means to the parties involved, so that confusion is avoided. This consensus about
what metadata can mean within a given discussion can be reached using a common set of terms.
When we are speaking of the headers or tags that are coupled with actual data, we are speaking
of metadata elements, which are often coupled with values of some kind. Common elements
refer to the time or date of creation, to the identity of the creator of a work, or to the medium or
mediums that were employed to present the work. A schema is a unified set of elements,
assembled for a specific purpose and generally deployed with explanations of what each element
is intended to describe so that overlap between elements is avoided.
To illustrate the foundational ideas in a discussion of metadata, we look to Chen’s Entity-
Attribute-Relationship model (1976). It is the basis for modern understanding of database
construction and activity. It describes Entities – discreet items of data or information – and
Relationships – how these discreet items are related to each other. Attributes are the values that
represent either the Entities or Relationships in this model. These items are represented in
graphical form, allowing for a visual map of a database that would allow errors to be seen and
flaws to be corrected. This model is certainly the basis of modern relational databases, where
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Entities are now called fields, Attributes are the data within the fields, and Relationships are the
links between a field in one table and the primary key in another.
By itself, Chen’s model is not a metadata schema; it is a forerunner of current metadata
schema, a rudimentary but effective set of rules for what pieces of information can be recorded
and how. It introduces the concepts of hierarchy and ontology to database design, and can serve
as the basis for any discussion of metadata in general. Chen’s model applies to metadata through
its illustration of the difference between values (called Attributes in Chen’s model), elements in a
schema (called Entities in the model), and metadata schema (the model itself).
Luang, Hibler, and Mwara (1991) propose using their Picture Description Language
(PDL) to describe the visual focal points of an historical image; this system is based on Chen’s
Entity-Attribute-Relationship model and roughly corresponds to the nouns, adjectives, and verbs
in English grammar. Attributes of those objects – adjectives, quantity modifiers, and such – are
then taken; and Relationships among the Objects are then accounted for, sometimes with
multiple relationships among the them; and Events, which, while perhaps composed of the
preceding three items, may capture the meaning of the image as a whole. PDL is the basic,
rudimentary model that all such present-day solutions to the problem of describing images
follow: identify the relevant constituent parts of the image, describe the image in some useful
way, and define what relationships exist between those parts.
Tam and Lueng (2001) describe Structured Annotation as a way to describe images using
simple, present tense sentences based on five components: Agent, Action, Object, Recipient, and
Setting, with descriptors of each as needed. Like Chen’s model, this is a less formal schema for
image description than found elsewhere, but they find that its implementation in a database is
both straightforward and useful, and is a way to accommodate all 12 of Jörgensen’s classes. This,
coupled with an external authority file (such as the AAT or LCSH) would allow for links to be
made between or among other thing: agents and political positions, dates and settings, or objects
and historical significance. While this is more appropriate for the description of editorial
cartoons than is Chen’s, it is still just a model, not a fully formed metadata schema.
2.3.1.1 Metadata as a concept And yet, even if we agree that these terms are
reasonable to use and we further agree about their meaning, there remain variations on what
metadata is and what it is for. Caplan (2003) traces the history of metadata from the 1960’s to its
being trademarked in 1986, then gives more detail to its rise in the 1990’s in computer science
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and federal government work, and from there to online applications. She describes a definition of
metadata as elusive, saying “… there is no right or wrong interpretation of [the word] metadata,
but that anyone using the term should be aware that it may be understood differently depending
on the community and context within which it is used” (p. 3), going on to describe it for her
purposes as structured information that describes an information source.
Gilliland (n.d.) describes metadata as the representation of an information object in
whatever ways are deemed appropriate by the indexer, echoing the flexibility (or ambiguity) of
the word “metadata” found with Caplan. She states that until the mid-1990s metadata was the
concern of geospatial specialists and those involved with the back end of database management,
but that the term has come more to the fore as the Information Age becomes more and more a
reality. She finds that metadata is “the sum total of what one can say about any information
object at any level of aggregation” (p. 1) and that it is important because it increases accessibility
to the data it describes, helps to retain the context in which the data is generally seen, and it helps
to preserve data when it migrates from one system to another, among other reasons. She
concludes that while the evolution of metadata has allowed us to better describe what
information we have, it has not absolved us of the need to scrutinize its use or to anticipate user
needs. This example combines references to a set of elements (a schema) and, by inference, the
rules for using those elements, as well as the data itself.
The ALA’s Committee on Cataloging: Description and Access (1999) found, like
Gilliland, that metadata describes information-bearing entities (IBEs), but their definition breaks
with Gilliland where intent is concerned, stating that it is generated with an eye toward naming,
finding, and administering a collection of such items. The ALA calls metadata “structured,
encoded data that describe characteristics of information-bearing entities to aid in the
identification, discovery, assessment, and management of the described entities” (p. 1, sec. 3),
and states explicitly that the charge of the Description and Access committee is to work toward
developing standards for use within the various MARC formats. Again, reference is made to the
schema (“structured, encoded”); one difference is in the description of the purpose of metadata,
that it should be a helpful tool in the hands of users and indexers alike.
Burnett, Ng, and Park (1999) defined metadata as, “… data that characterizes source data,
describes their relationships, and supports the discovery and effective use of source data” (p.
1212), continuing the previous theme of “data about data,” mirroring the ALA definition where
55
intent is concerned, and adding that the relationships among data can be described as well. They
describe their efforts to look at metadata from the traditional bibliographic point of view and
from the more modern data management point of view, noting that the former focused on data
modeling and bibliographic control where the latter focused on data use in both the short and
long term. Rather than concentrate on the differences between the two approaches, the
researchers found that the two veins overlap in many areas: both wish to render a collection of
data more accessible, both try to describe the contents of the collection as accurately as possible,
and both seek to aid users in the querying of the collection and accessing the data within it.
All four articles describe metadata as a smaller set of data that describes a larger set of
data. Burnett, Ng and Park aver that it can be used for traditional information organization
purposes as well as for enabling data use and management, a notion the ALA seconds in stating
that metadata can be used for both administration and for the discovery of connections between
documents. We can add to this the idea of flexibility offered by Caplan and by Gilliland when
they state that the indexer and, by proxy, the indexing institution can amend metadata to fit its
own needs. For the purposes of this work, we will combine these definitions of metadata,
defining it as “summary data that represents a document for the purposes of identifying that
document within a collection, for meeting the needs of the collection’s audience vis-à-vis
comparing and contrasting such documents, and for administering a collection of documents”.
2.3.1.2 Metadata – types and functions Just as there were threads of similarity and
difference in several definitions of metadata, we find that there is an analogous situation where
the various types of metadata are concerned. Caplan (2003) found that there are three kinds of
metadata, Greenberg (2001) found that there are four, Gilliland (n.d.) found five, Lagoze, Lynch,
and Daniels (1996) seven, and the IEEE (Institute of Electrical and Electronics Engineering,
2002) nine. While these five pronouncements of the types of metadata found different number of
labels, they all concentrated around four similar functions (Table 3).
Table 3
Comparison of Metadata Types Authors (# of functions described)
Functions
Descriptive Administrative History Structural Rating
Caplan (3) Descriptive Administrative Structural
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Table 3 - continued Authors (# of functions described)
Functions
Descriptive Administrative History Structural Rating
Greenberg (4) Discovery Administration Use Authentication
Gilliland (5) Descriptive Administrative Technical Preservation
Use
Lagoze, Lynch, & Daniels (7)
Descriptive Administrative Terms & Conditions
Provenance Linkage Structural
Content Rating
IEEE (9) General Educational
Rights Technical
Lifecycle Annotation
Relation Classification Meta-metadata
Note: The five categories along the top of the table are general descriptions of the kind of metadata types found among the authors. With these
columns are the actual labels given by the authors to what each considered the different kinds of metadata. The numbers beside the author’s names denote how many different kinds of metadata that author found to be distinct.
This illustrates what can be found throughout the literature, regardless of the number of
“types” of metadata that are purported to exist: all descriptions tend to center on the same three
basic categories – Descriptive, Administrative, and Structural – while many also focus on the
History of the document, both within the collection itself and from the time before the document
was acquired. The first is largely concerned with enabling the collection to be accessible to users,
the second with the maintenance and growth of the collection, and the third with the collection’s
use. Again, we find that there is a certain flexibility to be found in metadata schema, partly
depending on the purpose and composition of the collection, and partly on what the collection
consists of.
The IEEE describes metadata in general while focusing on what it calls “learning
objects” and caters to concerns of publishing lessons and classroom activities in its schema.
Lagoze, Lynch, and Daniels speak to concerns of developing and deploying metadata schema for
collections of multimedia files and sets of files, thus including Ratings as a separate category
when no one else included it at all. Gilliland seems to represent the interests of the collector more
so than those of the user, both Greenberg and Caplan vice versa, and all three approach their
description of metadata categories as educators, describing what is likely to be found in the
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working world in general terms and leaving the specifics of a given situation to be determined as
needed. While any of these categorizations of metadata is arguable, they are also all defendable,
depending on the situation in which they might be employed.
2.3.1.3 Metadata schema Different than metadata itself is a metadata schema.
Broadly speaking, a metadata schema is a set of metadata elements designed to record the chosen
elements – and how they are meant to be used – in a coherent and comprehensive way, to
communicate the overall representation of the document to those searching the collection, and to
assist in collection management. It represents the decisions made by the collector of a set of
documents as to what must be recorded to facilitate both the identification of unique documents
and the assembling of similar documents from within the collection, to ensure the proper use of
the documents within a collection, to record the history of the document within and without the
collection, and to help paint a picture of the collection as a whole. To be clear: one single
metadata element cannot contain all of the necessary information for any one of these areas. A
metadata schema is (one hopes) a well thought-out set of elements that address the collection’s
needs in each of these areas, and possibly others besides.
The IEEE (2002) finds that a metadata schema defines the structure of the metadata for a
given document. For this organization, a schema brings together the several types of metadata
and provides a standard way of implementing the description of the various items in a collection,
naming the elements which may be used and grouping similar elements together. Their view also
specifies that while the values given for any particular metadata schema are not specified, the
nesting of categories and subcategories is, and that these should not be altered.
Greenberg (2005) reports that unlike metadata itself, a metadata schema is a less
universal term, but is generally described as a collection of metadata elements, a container for
metadata, or a tool designed to serve a purpose. She states that in years past the terms scheme or
schema had been applied to large taxonomies like the Dewey Decimal System or the Library of
Congress Subjects Headings, but that now it generally refers to data structures or container-based
descriptions systems. Greenberg finds that a metadata schema is:
1. A collection of metadata elements gathered to support a function, or a series of
functions (e.g., resource discovery, administration, use, etc.), for an information
object.
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2. A collection of metadata elements, forming a structured container, to which
data values are added. Data values may be uncontrolled or controlled (e.g., taken
from a source such as LCSH or a standardized list of values).
3. A collection of data elements, with their attributes formalized in a specification
(or a data dictionary). Examples of element attributes include the metadata
element’s “name,” “identifier,” “label,” “definition,” and the “date the element
was declared.” (p. 24)
The National Information Standards Organization (NISO) (2004) finds that a metadata
schema must serve a purpose, and that purpose is to organize a given set of information. To this
end, metadata schema name the specific elements that comprise them, and may or may not give
guidelines for how those elements are to be populated. Also, NISO finds that metadata schema
can be syntax independent, meaning they have no specified way of being implemented, or syntax
dependent, meaning they require the use of, for example, XML or SGML to be properly rendered
for use.
Compared to the definitions of metadata and the types of metadata, the definition of what
schema are is relatively straightforward; metadata schema are organized sets of elements that
serve the needs of a particular collection or type of collection in terms of administering it on
behalf of the collector and making it searchable on behalf of users. Also listed are some best
practices: a schema should provide structure, function, decisions about controlled or uncontrolled
values in elements, meaningful element names, and a schema should stand on its own,
independent of how it might be implemented.
2.3.1.4 Current Relevant Metadata Schema In her analysis of 105 metadata
schema, controlled vocabularies, and other related works, Riley (2010) illustrated which works
can be applied to various domains, communities, functions, and purposes. Each of these areas is
wide-ranging and includes a number of areas which have nothing to do with indexing editorial
cartoons: while important and worthy of attention, geo-spatial metadata schema, libraries’
indexing needs, record formatting issues, and technical metadata description are not particularly
relevant to discovering the basic issues of how users describe such images. The relevant areas,
as described by Riley and within the realm of metadata in general, would be both the Cultural
Objects and Visual Resources sections of the Domain area, the Structure Standard section of the
Function Area, and the Descriptive Metadata section of the Purpose area. The four basic
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metadata standards that are listed as having a strong association with all four of these areas are:
the W3C’s Ontology for Media Resource, the Dublin Core and Qualified Dublin Core, the Visual
Resource Association (VRA) Core, and the Categories for the Description of Works of Art
(CDWA) and CDWA Lite.
The W3C’s Ontology for Media Resource 1.0 (2009) is a work in progress, a point
emphasized from the beginning of the document. Its purpose is to create a vocabulary for Web-
based media resources whose categories are “defined based on a common set of properties which
covers basic metadata to describe media resources” (Ontology for Media Resource, Abstract)
and to map between commonly found concepts in already extant metadata schema. The
vocabulary covers several common concepts, such as identifying the creator of a work or the date
the work was completed, that are found in most schema, and has a few concepts, such as
framesize and compression, that are specific to Web-based media. This particular Ontology
would do a fair job at describing editorial cartoons, coving the main Structural and
Administrative points described by Caplan, but would be less useful in providing Descriptive
metadata. Greater than this is the twofold problem of the work being incomplete and that the
work is not a formal schema, but rather a glossary of common elements for Web-based media.
While the accuracy of the Ontology is not being questioned, its usefulness in describing editorial
cartoons is.
The Dublin Core (2007) places an emphasis on simplicity; by its own description, it does
not deal well with complex relationships between items. It is designed for use in multiple
scenarios that seek to describe networked resources; while primarily used for Internet items, it
can also be used in businesses, libraries, museums, and so forth as “a small language for making
a particular class of statements about resources” (¶ 1.2). Dublin Core’s 15 elements and 24
additional qualifiers (together comprising the Qualified Dublin Core) are user-oriented and are
meant to provide basic-level descriptions in whatever setting they are used. The Dublin Core’s
development is guided by three general principles: The One-to-One principle, which states that
each instance of a given document (the original, a duplicate, an image of the document) must
each get their own metadata description; the Dumb-Down principle, which states that any sort of
qualifier must be able to stand on its own as a description of the document; and the Appropriate
Values principle, which is taken here to mean that the indexer must assume that the reader of the
metadata will be human, not a machine. But the Dublin Core’s inability to deal well with
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complex relationships (like those found in and between editorial cartoons and a given event)
lessens its value as a schema for cartoon description; coupled with the focus in its origination and
continued emphasis on Web-based information sources, this makes the Dublin Core less than
ideal for editorial cartoon description.
The Visual Resources Association Core 4.0 metadata schema (2007) is designed to
catalog cultural objects in a structured format. It presents rules for the use of each element and
possible ways for an organization to use the elements hierarchically: “The element set provides a
categorical organization for the description of works of visual culture as well as the images that
document them” (VRA_Core_4.pdf, Introduction). It is also specifically designed to be
expressible using XML. Its 18 elements encompass descriptive, administrative, and structural
elements for use in museum or library settings. The VRA points out specifically that this schema
will not provide inter-indexer consistency in and of itself, that it must be used in conjunction
with some metadata authority in order to reach its highest potential, though it can be used
without one. As the VRA recognized that some of the documents within a given cultural
collection consist of several images or of parts of an object, the element Relation has been
introduced as one of several major changes from the previous VRA standard. It has a reasonably
detailed data dictionary, it does not require the use of an authority file, and it is designed for the
cataloging of such things as editorial cartoons. But while the VRA Core provides a place for
textual words within a work to be recorded, it does not adequately address the different kinds of
words found in an editorial cartoon; it is not difficult to conceive of a need for differentiating
between those words found in the caption and those found in a speech bubble, for instance.
Additionally, it does not provide a ready method for describing the multiple new events that
would be part of some editorial cartoons. The VRA Core could be used to describe editorial
cartoons, after some modifications were made for these specific kinds of images.
The CDWA -- Categories for the Description of Works of Art (2011) – was developed by
the Art Information Task Force at the Getty Trust with funding from the National Endowment
for the Humanities and the College Art Association. It has two companion works: CDWA Lite,
an XML implementation of the core categories of the full CDWA; and CCO – Cataloging
Cultural Objects – a data standards work prescribing both methods of art description and values
to be used in certain fields or categories. As expected, it deals well with each of Caplan’s three
categories of metadata in the core categories (considered essential for retrieval purposes) and
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supplements them in the expanded categories which can be used for both collection retrieval and
image or art display. These additional categories help the CDWA address Greenberg’s four types
of metadata, as well as those of Gilliland (five types), Lagoze, Lynch, and Daniels (seven types),
and most of those for the IEEE (nine types).
This schema would do well in describing all aspects of editorial cartoons, save two: it
does not provide a sufficient place for the listing of words found in such cartoons, and it does not
provide a ready place for the actions depicted in a cartoon. Granted, the Inscription/Marks
category of the full CDWA would be where all quotes, captions, labels, and other such writing
would be described, but there is no way to make known which words belong where; as it stands,
the CDWA does not provide a method for differentiating between a caption or a person speaking
(much like the VRA Core), unless we were to add such things to a subcategory, such as
Inscription Type. And there is no place to show what actions, if any, are taking place in the
cartoon which, given the type of image, could carry meaning vital to description and
interpretation. In any case, it is plain that the CDWA was not created with describing such things
in mind, and that the method provided for dealing with such information in an editorial cartoon is
less-than optimal.
The CDWA has one significant advantage over the other metadata schema examined: it
has a method for describing both the subject, and – importantly – the subject authority, to be
provided within a metadata record. Under the CDWA category Subject Authority, the Subject
Name might be the headline of the story that likely inspired the cartoon, the Name Source would
be the publisher (whether traditional newspaper or online provider) that both the story and the
cartoon appeared in (although these two items would likely appear on different dates), and the
Broader Subject Context could possibly be used to frame the cartoon in historical terms, as they
might develop over time. Of those schema examined, the CDWA is the best one to use when
describing editorial cartoons.
Even when given a fully-formed schema with which to describe editorial cartoons, we
must wonder if the essential aspects of such images are being properly represented. While it can
be assumed that museums can adequately collect and display salient information about the items
in its collection using CDWA, VRA Core, or other such schema, is it right and proper to assume
that these elements would apply equally to editorial cartoons? Or would it be better to assume
that some further adjustment to the schema would be needed, so that these particular kinds of
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images would be fully indexed? And if the latter is so, how might we best go about determining
what those elements should be?
2.3.2 Folksonomies
One possible way that this information could be gathered is from the implementation and
analysis of a folksonomy specifically for editorial cartoons. In some instances, a folksonomy is a
loosely organized set of descriptors provided by volunteers for various instances of a given type
of phenomenon. Flickr (2010) is, in this case, a collection of photographs and other images that
have been posted by an “owner,” who has then allowed others in the community to “tag,” or post
short comments about, the images in the owner’s collection. In other instances, a folksonomy
refers to the environment in which this happens which, in most cases, is provided by various
collaborative technologies. These allow an entity, such as Flickr, to provide the ready and easy
means of image dissemination and for data collection and subsequent analysis.
In either case, tagging is the central activity in a folksonomy. This is what the participants
in a folksonomy produce, evidence of their thoughts about a given item, whether that item is an
image or a website or what have you. It is these tags, taken in aggregate, become a tag cloud, a
graphic display of terms used to describe a document, with the size of the letters indicating the
popularity of the tag. In some folksonomies, previously used tags are presented at the time of
tagging, allowing those who follow to simply click on those already-present tags instead of
having to type in their own. In other systems, this is not the case, and each participant must
provide their own tags, even if they have been previously used by others. In both cases, the
choice of what words to use to describe an item in a collection is left to the users; no effort is
made to control a vocabulary or to correct mistakes. The idea is that, collectively, both the proper
terms for item description and the aspects of the item that need to be described will emerge from
the amalgam of terms produced in a folksonomy.
2.3.2.1 Definitions Vanderwal (2007) defines folksonomy as:
… the result of personal free tagging of information and objects (anything with a
URL) for one's own retrieval. The tagging is done in a social environment
(usually shared and open to others). Folksonomy is created from the act of tagging
by the person consuming the information (Vanderwal, Definition of Folksonomy).
He relays how the term came about in the first place, combining the idea of taxonomy as an
information organization paradigm with the idea of an anarchic and democratic self-perpetuating
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group of interested people. Vanderwal also notes that the emphasis in such activity is on
description, not classification, a fact that becomes important as criticisms of folksonomy arise.
Bruce (2008) describes a folksonomy as an uncontrolled vocabulary, allowing for
expansion and scalability on the one hand, and ease of evolution as the use of language changes
on the other. To help determine the usefulness of such systems, he compared the tags given to
mutually described documents in both CiteULike and in ERIC, finding that there was a
commonality of tags only 7.6% of the time. This, says Bruce, shows that folksonomies are good
ways to supplement traditional description practices, one that keeps up with changes in language
and outlook and that is done far more cheaply than producing changes in standardized systems.
Macgregor and McCulloch (2006) contrast controlled vocabularies and their abilities –
linking synonyms, differentiating homonyms, enabling truncated searches, and correcting for
spelling variations – with collaborative tagging, which they define as a “practice whereby users
assign uncontrolled keywords to information resources” (p. 293). The fundamental problem with
controlled vocabularies, they say, is that the propagation of information resources is moving
faster than the ability for the vocabulary to keep up with needs, while the problem with
collaborative tagging is that there is no control of the vocabulary at all, resulting in far more
noise in a search than would otherwise be the case. They conclude that while it is unlikely that
collaborative tagging will replace controlled vocabularies in libraries, databases, and other such
collections of information, it can be used as a way to engage the users in the maintenance and
development of controlled vocabularies, and as a way to provide some measure of ownership to
the users and patrons of institutions, and providing complement and supplement to controlled
vocabularies, echoing Bruce.
For the purposes of this dissertation, we will define a folksonomy as the practice,
environment, and result of the democratic and uncontrolled tagging of documents with words or
phrases for the purpose of determining the salient characteristics of that document through
counting the number of taggers who agree with a given tag. This is generally done in a Web-
based environment, usually displays up-to-the-moment results, and often allows for the exchange
of ideas between taggers on an individual level.
2.3.2.2 Criticisms While we might admire the sentiment behind folksonomies – that
the people will speak and be heard – it has been found that, in practice, there have been some
problems that have proven to be pervasive and an obstacle to the aims of a folksonomy’s
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community, whether those aims would be to gather information or to focus on the various
aspects of “community”. If the aim is to gather information, there are sometimes those who wish
to sabotage the effort. If the aim is to build a community, there are sometimes those who
passionately and perhaps obstinately wish to argue the minutiae of the data; one example of this
is the conflict resolution practices found in Wikipedia, complete with its own vocabulary, formal
procedures, and known methods of gaming the system (2010). While these things center on the
function of a folksonomy, more effort has been put into examining the flaws in the results of
them.
In his 2007 editorial, Alireza Noruzi defines a folksonomy as “… a user-generated
taxonomy used to categorize and retrieve web content such as web resources, online photographs
and web links, using open-ended labels called tags” (p. 1), putting his view of folksonomies at
odds with others in that he explicitly sees them as taxonomies where most others seem to view
them as ontologies. He advocates for the use of thesauri in folksonomies to correct users errors,
to provide an alternative for the problems of plurals, polysemy, synonymy, and specificity, and
to bridge the gap between those who populate the systems with tags and those who simply use
the system to search for documents, stating that while folksonomies are exciting and new, that
they are not a panacea to information representation problems.
Guy and Tonkin (2006) found that the folksonomic flaw is that tags are “ambiguous,
overly personalized, and inexact” (sec 2, ¶1), and that while they may help the tagger that made
them, they do not really help identify documents in a search because of the lack of traditional
disambigufiers (synonyms, accounting for tense, etc.). They suggest that improving tags for
search would involve a two-pronged approach: educating users as to the preferred and more
useful composition of tags, such as using the singular, the present tense, and proper spelling; and
what amounts to technical considerations, calling for the ability to use multi-word tags or phrases
as opposed to the current requirement in most systems to have tags be all one word, using
underscores in a phrase where spaces would normally go. Thus, where Noruzi calls for the hand
of the information professional to improve the utility of folksonomies, Guy and Tonkin call for
improved technological capabilities and user education.
Peterson (2008) contrasts traditional subject cataloging and folksonomies in two ways.
First, she points out that where the former is concerned with placing the document in the proper
place in the system according to a set of rules, the latter is concerned with describing the
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document on an ad hoc basis based on what seems right to the tagger; this outlook is similar to
that of Vanderwal when he pointed out the difference between classification and description.
Second, traditional subject indexing generally leads to a taxonomy of some sort, where
folksonomies generally lead to ontologies. Peterson goes on to say that there are several efforts
underway that combine both traditional subject cataloging and folksonomies in an effort to
expand the user’s ability to find things in those systems (this combination being yet another
effort to improve folksonomic utility), and to discover how users use the system in the first place.
Peters and Weller (2008) describe a way to transform folksonomies into something more
accurate and efficient for searching activities, a group of methods that explicitly follow the
metaphor of gardening, where each of the tags are plants and the whole folksonomy a garden.
They find that automatic spam removal is akin to using pesticides, and removing or correcting
bad tags as weeding, both of which are common calls for making folksonomy’s results more
useful. Peters and Weller also call for the purposeful placing of new and improved tags into
already existing tag clouds when they might contain popular tags that are too vague, a practice
they call seeding, and the placement of thesaurus-like terms to direct users to better terms or
practices, which they call landscaping. They also realize that the manual maintenance that all
these practices engender would be difficult at best to implement across any folksonomy,
although they point out that those communities that are small enough and united enough in both
purpose and as a community unto themselves may be able to gain some ground in these areas.
This article serves as a good summation of the other’s calls for improving the results that come
from folksonomies. Some center on the use of technologies, others call for the interceding hand
of the information professional. But none seem willing to take the tag clouds generated by a
folksonomy as the be all and end all of document description.
2.3.2.3 User Behavior But where most of what had been discussed focuses on the
usefulness of the folksonomy’s product, other research has instead concentrated on how the users
themselves behave in a folksonomic environment. This is in part shaped by the tendencies of
online behavior in general, and partly from the participants in a folksonomy being given power
over the outcome of an effort, even if it is just a small part in it.
Golder and Huberman (2006) contrast collaborative tagging and taxonomy, noting that
the latter is the traditional form of subject cataloging and that it requires that a set of rules be
followed in order to manage a collection through hierarchy, while the former allows the users of
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a system to label a document as they wish, a practice that, through accumulation of tags, allows
for a fair description of a document. They then explore user behavior in folksonomies. They
examined 212 URLs and 19,422 tags from del.icio.us, a webpage bookmarking and commenting
site, finding that user’s activity in the bookmarking site can vary greatly in terms of the amount
of activity shown, the purpose for which the activity is undertaken, and the kinds of tags used.
They also found that, after an initial period of user attention, a stable pattern of tags emerges
over time where the proportion of users applying a given tag becomes static.
Kipp and Campbell (2006) acknowledge that there is a lively and vigorous debate about
the appropriateness of folksonomies in formal description systems, but find that “Untrained users
will not, of themselves, produce rigorously-designed thesaural structures; we need to determine
whether the results they do create are useful anyway” (p. 2). In another analysis of user
tendencies, they analyzed 64 URLs from del.icio.us with 18,904 unique tags used over 165,000
times, counting the number of times two users both used the same tag for the same image. They
found that while users would often use the same single tag, they used the same set of two or
more tags far less often. They also found a less radical than normal power law distribution in tag
usage among all URLs, with the first seven tags being frequently used before a steep drop-off is
seen. Kipp and Campbell conclude that some common tags would not normally be found in
traditional subject description systems (for example, tags that serve as personal reminders to
individual taggers), that tag application is non-conventional and inconsistent, and that closely
related terms do not necessarily occur together with any reliable frequency.
Lee, Goh, Razikin, and Chua (2009) seek to uncover the relationship between one’s
familiarity with social tagging and the effectiveness of that person’s tagging. Using results from
a previous study, they asked 262 subjects to assign pre-selected tags to pre-selected images; the
tags had already been deemed appropriate to the images in the previous study, but the
participants were not privy to this information. Subjects were also asked about their familiarity
with web directories, search engines, and social tagging systems. They found that those with a
high familiarity with such Web-based information collections placed tags with the correct image
more often than those unfamiliar with the concepts, suggesting that a user’s familiarity with the
social tagging environment tends to result in better tags from that user.
Instead of looking at the behavior of users within the folksonomic environment, Stvilia
and Jörgensen (2009) examined different types of user groups. They compared tagging practices
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of collections of photographs on Flickr, a Web-based image sharing and tagging site where some
images were administered by an individual (called photosets) and others by a group. They found
that, in general, the tags applied to images by a group are more homogeneous than those found in
photosets. They also found that photosets tended to tagged according to activity, thing, place,
photographic technique, and person (in that order), where group collections were tagged
according to thing, place, photographic technique, person, and concept (again, in that order).
Some of the data found in this study compares favorably to that generated by Jörgensen in her
1995 study: thing, technique, person, and activity are all represented in roughly equal proportions
in both the group descriptions in 2009 and the describing tasks in 1995, and the photoset
descriptions of person, technique and thing are similarly proportional to her sorting tasks from
previous research.
Given the dearth of research into the indexing of editorial cartoons, the next best place to
search for examples of how cartoons should be described was metadata in general, and the
various metadata schema in specific. But this proved to be less than perfect as a starting point for
image description, for two reasons: the schema were not specific to editorial cartoons, leaving
out some potentially important elements, especially regarding words within the image; and, more
importantly, the lack of data regarding what users would want to see described. While we might
be able to adapt metadata practices to describe a cartoon, there is no way to know if the elements
included would be those that would be useful to either the collector of such images or to those
who might use that collection. Bates (1998) said that the indexer’s experience is different that
than of querier; so too, it seems, is the tagger’s experience different than that of the information
professional.
Folksonomies could be used to provide the right environment for the collection of such
data. Granted, it would be a short-lived, goal-specific kind of folksonomy, one that would not
persevere and become a cultural and social phenomenon like Flickr, but rather one that would be
created specifically for the purpose of seeing how people choose to describe editorial cartoons
when they have neither a template to fill out nor a guide regarding what to describe. In this way,
a folksonomy would provide the means to solicit and collect such information in an anonymous
manner, and when deployed in its usual electronic collaborative environment, would allow for
the folksonomy to reach far more people than a standard paper-and-pencil method. While a
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folksonomy might not produce the desired end product for editorial cartoon description, it allows
us to begin to investigate what could and should be done in future efforts.
Thus, we come full circle: we began by examining what the literature had to say about
indexing editorial cartoons and how collections of such images were being organized, moved to
the pitfalls and axioms of image organization, and we end by seeing how collections of
documents in general might be organized by naïve users in less than traditional formal systems.
Taken together, it points to the idea that useful information about user’s needs and tendencies
can be garnered from the users themselves, with the focused efforts of an information
professional to process the raw data from the users into something both more useful and more
accurate.
2.4 Not relevant at this time
There are a number of fields of study that might profitably comment on the description of
editorial cartoons, but that are not included here. They are not included in this research for any of
several reasons: overlap with a more potentially useful field that has already been included;
being enough degrees removed from the field of library and information studies that a good fit
would be difficult to make at this time; insufficient focus on the specific issues surrounding the
indexing of editorial cartoons; or any combination of these.
2.4.1 Cataloging
Cataloging theory and practice could be seen as another avenue of approach for cartoon
description. The International Federation of Library Associations (2009) states that a catalog
should enable users to find, identify, select, and obtain documents from a collection, and to
navigate within the collection. Hoffman (2002) differentiates between cataloging – “the
preparation of bibliographic records and making them accessible to readers in an orderly
arrangement so that the resulting index to the library’s holdings is clear, consistent, and
comprehensive” (p. ix) – and classification, which is the activities involved in arranging the
documents in a collection in the physical space provided for them. Read (2003) finds that
cataloging is “… the art (or, some might say, the science) of describing a document or object in
the smallest possible number of words” (p. 5) with a twofold function: to list the contents of a
collection, and to assist in finding things in that collection. Above all, says Read, a catalog must
be accurate, clear and consistent. While cataloging may be applicable to later iterations of
research in this area, it is not sufficiently relevant to improving the state of art of editorial
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cartoon description in general or the purpose and direction of research in the area specifically to
warrant review at this time.
2.4.2 Archiving
Where cataloging deals more with the collection of individual items to form a collection,
archiving deals more with the collection as a whole and the organization or individual that the
collection represents. Smiraglia (1990) found that archiving and bibliographic control have a
number of similarities, among them identification, collection, and evaluation of the items in a
collection, but that where archives seek to facilitate collection, libraries seek to facilitate
identification. Fox (1990) sees archives as embracing all types of media, verbal and otherwise.
He finds that archives are more about the collections as a whole, rather than the individual items
in it. Yakel (1994) finds that the word “archive” means three separate things: non-current records
of some value, the agency that is responsible for those records, and the physical facility that
houses the collections and (often) the agency that runs it. Ham (1993) sees the archive
development process as a never-ending threefold cycle: acquisition of materials through transfer,
donation, or purchase; accession of the materials into the collection, and appraisal of the legal or
historical value of the materials, something described by all of these authors as the most difficult
part of the process because of the difficulty of discerning what will help paint a picture of the life
and times of the organization or person in question. Again, while this may well be relevant in a
situation where a cartoonist’s collection is being whittled down with an eye toward representing
a long career or the depiction of an event, it is beyond the scope of this dissertation because it
deals more with the end-of-lifecycle issues of a collection than it does with the description of the
cartoons in the first place. While possibly relevant to future research, it will not be examined in
this work.
2.4.3 Information Retrieval
Another field in library and information studies that might have something relevant to say
about describing editorial cartoons is information retrieval. Meadow, Boyce, and Kraft (2000)
examine the act of retrieving information, which in modern instances is a communication process
between a querier and a system; one without the other does not constitute information retrieval,
in their view. Chowdhury (2004) states that, initially, information retrieval was really document
retrieval since systems were designed to facilitate the discovery and delivery of documents from
a collection, but that advances both in technology and in document descriptive practices have
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made the retrieval of information easier and more efficient. Ingwersen and Jarvelin (2005) aver
that there is a list of potential concerns when speaking about information retrieval and its
supporting systems: the information objects themselves; an information space where they can be
stored; an information retrieval system, which will aid in finding and delivering the information
being sought; some sort of interpreter, who will describe the information object to the IR system;
and the context in which all of this will take place (p. 19). While some of the principles and
concerns listed here would be relevant to the building of a cartoon retrieval system, we are not
yet at the stage in the research that such systems can be meaningfully contemplated. Information
retrieval, like cataloging, might be relevant in future work and research, but is not relevant at this
point.
2.4.4 Content-based image retrieval
Jörgensen (2003b) described content-based image retrieval (CBIR) as the first method
used to describe images and subsequently retrieve them, using the physical characteristics of the
images themselves; color, saturation, and hue were first used as methods of describing images so
that similar items could be brought together. Later, such low-level features of electronic images
as texture (although the results in this area have been less than satisfactory) and shape were used
to describe images. Enser (1995) elucidates on CBIR in a roundabout way, commenting on the
use of surrogate or sample images to have computers find other images like them, and the
development of machines that automatically extract shapes and spatial relations within a given
image. Greisdorf & O’Connor (2008) describe such technical details of images as “metadata,”
and state that such information can be more of a hindrance than a help to end users, while it may
retain value to the keepers of the collection. While recent advances in CBIR may help to identify
faces (such as President Obama’s) or shapes (such as the White House) in still photographs, its
ability to identify such things in editorial cartoons is compromised by the freehand drawing of
such images, and by the over-the-top visual characterizations of political figures in general.
2.4.5 Word and Image Studies
Mitchell (1996) describes contemporary “word-and-image studies” as having to do with
the similarities and differences in how both words and images communicate meaning, including
syntax and grammar of both the written word and of visual composition; specifically, he calls
word-and-image studies a “… a kind of shorthand name for a basic division in the human
experience of representations, presentations, and symbols” (p. 47). Varga (1989) describes word-
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and-image studies as speaking to two things: object-level relations, concerning such things as the
words and the image appearing simultaneously or consecutively, whether the document in
question is part of a series, and whether the words and the image are inseparable or distinct; and
metarelations, concerned with the art historical and contemporary commentary made on the
document. Weingarden (1996) uses word-and-image studies to counter some of the shortcomings
she sees in iconography as an art critical technique, balancing the interpretation of both the word
used to describe and interpret the image and the visual grammar shown in an image. This area of
study is beyond the scope of this paper because of the comparative nature of the field; where the
subject indexing of editorial cartoons calls for the interpretations of cartoons and the extraction
of salient points, word-and-image studies call for the comparison of how both images and words
can get the same point across.
2.4.6 Research simply about cartoons
There are a number of articles, books, and other works dealing with editorial cartoons in
several ways: their history, the decline of the editorial cartoonist, interviews with cartoonists
about their craft, and so on. While interesting and informative, these articles have little to do with
indexing editorial cartoons; they are so far removed from the focus on this area that, while they
can serve as background material for a history of the art, for instance, they will not be considered
as sources that can meaningfully shed light on the subject at hand. See Hauck (2006) for an
example of such articles. If this were a treatise on the history of editorial cartoons, on how such
images have changed history, or the current state of editorial cartooning in America, then such
articles would be a magnificent way to begin research. But this research is focused on the
indexing of such images, and as such has little place for these related, but ancillary, items.
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CHAPTER 3
METHODOLOGY
3.1 Overview
This research used content analysis to derive categories of descriptors from both a
tagging activity and a simulated query activity. The cartoons of the five most recent usable
Pulitzer Prize winning cartoonists were used. Participants were drawn from academic professions
that are presumed to have an interest in the description of cartoons, and from serendipitous
participation of non-targeted audiences. The tagging activity and the simulated query activity
took place online using modified versions of the steve.tagger software (2006). The analysis was
based on Jörgensen’s 12 Classes, although the possibility of adding Classes on an as-needed
basis was left open. Interviews with both editorial cartoonists and image professionals were then
conducted to assess the degree to which this work conflicts with the expectations of those fields
and in what ways this research might influence perceptions and practices in real-world situations.
3.2 Research Questions
Four general research questions were addressed in this work. Three of these questions are
grouped together because they involve online participants, while the fourth stands apart because
it involves telephone interviews where comment was made on the data and the results of the
previous three questions.
3.2.1 How are editorial cartoons described in a tagging environment, and how do
the resulting tags map into Jörgensen’s 12 Classes?
This research question sought to discover how participants describe editorial cartoons in a
tagging environment. For this work, a tagging environment was one where a particular editorial
cartoon was presented to a participant who was then asked to provide key words or phrases that
describe the cartoon in question, without any guidance as to how such words and phrases were to
be determined. These tags were then analyzed according to Jörgensen’s 12 Classes (1996), which
have previously been used to categorize image descriptions for newspaper images (Laine-
Hernandez & Westman, 2006) and Flickr images (Brunskill & Jörgensen, 2002) in addition to
the illustrations used by Jörgensen (1995). These classes were used to categorize the participants’
tags, seeing what kinds of tags had been produced and in what percentages, then compared to the
aforementioned research.
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3.2.2 How are editorial cartoons described in a simulated query environment, and
how do query keywords and phrases fall into Jörgensen’s 12 Classes?
This question seeks to provide a different context for describing editorial cartoons by
switching from a tagging environment to a simulated query environment. Three weeks after the
tagging task, the same participants were asked to create simulated queries that they might use to
find the editorial cartoons in question using their favorite search engine, but they were not asked
to execute those searches, nor were they asked to provide the results from such queries. This
provides a different set of circumstances for cartoon description, and a comparison between tags
and queries may prove fruitful (see 3.2.3). Following the same procedures as the analysis for the
tagging activity, the substance of the simulated queries – what is left after, for instance, Boolean
operators have been stripped away – were categorized using Jörgensen’s 12 Classes, similar to
the analysis of image queries performed by Jansen (2007), Chen (2000) and Jörgensen (1995).
3.2.3 How do the tagging terms compare to the simulated query terms?
A comparison of the categories and their frequencies between the tagging environment
and the simulated query environment may show that the tasks net different results. Jörgensen
herself (1995, 1996) found that the frequency of classes changed between descriptive, querying,
and sorting tasks, results echoed by Brunskill and Jörgensen (2002), who used scientific
diagrams, and Layne-Hernandez and Westman (2006), who used journalistic photographs.
Discovering if this was true with editorial cartoons as well was appropriate because it may
expand the generalizability of Jörgensen’s 12 Classes, might further legitimize the notion that
different describing activities will yield different describing results, and could provide ways to
build better systems for image retrieval in general and editorial cartoon retrieval in particular.
3.2.4 How might these findings affect the practices of both editorial cartoonists
and image professionals?
Given the dearth of research concerning editorial cartoons, it may be that the results
generated from this study could be used to guide future efforts to communicate via such images.
To assess this, four editorial cartoonists and three image professionals were recruited to evaluate
and comment on the results of this study in an unstructured confirmatory interview. These
interviews do not represent another avenue of approach for the acquisition of new data for
analysis, an effort equal to or greater than the tagging and query phases to enlighten ourselves
about the 12 Classes, or an attempt to derive from the interviewees the greatest thoughts or best
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practices regarding image or cartoon description. Rather, they were designed to elicit predictions
from cartoon and image professionals vis-à-vis the 12 Classes, and to garner responses from said
professionals regarding the results of the two phases of the study.
Pursuant to this, the interviewees were given Jörgensen’s 12 Classes before the interview
was conducted and asked to rank the Classes in the order that they thought that they would be
used in the description of cartoons by ordinary, disinterested people. They were then given the
actual results, and asked if those results were in any way a surprise or if they would affect the
way they would communicate through their cartoons.
3.3 Data collection
3.3.1 Population
3.3.1.1 Tagging and query activities The population for this research was a blended
sample; one population consisted of academics in fields that were assumed to have an interest in
the research itself, and who were seen as likely to give a full, rich description of each image. The
second population consisted of non-degree holding participants, against whom these results
could be compared. Both were recruited for the first phase of the study (tagging phase) and were
invited back for the later query phase.
3.3.1.1.1 Degree holding population. The researcher recruited from the departments
and schools of a major research university in the southeastern United States for all these areas
except the last, where a physically proximate, non-research university was contacted. The
literature supports drawing participants for this research from the following academic fields:
Library and Information Studies: whether a subject's interest lies with cataloging and
classification (IFLA, 2009; Hoffman, 2002) or subject analysis (Fugmann, 1979; Maron, 1977)
in general, or specifically with image indexing (Jörgensen, 1996; Svenonius, 1994) or tagging
(Vanderwal, 2007; Bruce, 2008), this field is clearly one in which participants interested in this
research, and who may have something to contribute to it, can be found.
Political Science & History: the former is a field dedicated to the analysis of political
events and discourse (Farr & Seidelman, 1993). The latter is a field dedicated to the recording of
historical events and their consequences (Carr, 2002). Both have produced works that center on
editorial cartoons, both as a chronicle of the times (What America thinks, 1941) and a state of the
art (Hauck, 2006), and were thus presumed to have potential participants for this research who
would use cartoons as historical documents.
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Art History: The Art & Architecture Thesaurus (Petersen, 1990) and Cataloging Cultural
Objects (Baca, 2006) serve as fine examples of art history's need to consciously and consistently
describe art objects both within a given collection and between collections. Within art history,
word and image studies (Varga, 1989; Mitchell, 1996) examine the similarities and differences in
how both words and images communicate their ideas. Museum informatics highlights the advent
of sociotechnical interaction to the museum patron experience as well as the management of
collection information (Marty, 2008).
Journalism: Whether one subscribes to the more traditional idea of journalism advocated
by Lippmann (1925), where the role of the journalist is to act as a liaison between experts on a
particular topic and the public, or to the idea of “community journalism” espoused by Lauterer
(2000) where the events and issues of local interest are of paramount importance, it is clear that
editorial cartoons fall squarely in the realm of journalistic activity, as they are often first
published in some sort of newspaper or news magazine, electronic or otherwise. The collections
of Brooks (2011) and of Cagle (2009) show that a majority of cartoons are from newspapers, and
the Pulitzer Committee has only recently begun to consider non-newspaper based cartoonists for
the annual Prize for editorial cartooning (2011).
3.3.1.1.2 Non-degree holding population. At the beginning of the tagging phase,
the researcher was contacted by a member of the faculty of the primary university in question
regarding a potential second population that might participate in the research as participants. This
contact was completely unsolicited; the researcher did not know, had not met, and had had
absolutely no knowledge of the person initiating this contact. This faculty member sought to give
extra credit in his class of undergraduate marketing students for participation in this research. It
was felt that the opportunity to include a contrasting population, one that did not have a great
deal of advanced education and which could not be presumed to have an inherent interest in the
research and its results, might offer a differing point of view in the description of editorial
cartoons.
After an amendment to the Institutional Review Board to allow for the serendipitous
participation of non-targeted audiences, the protocol allowed for these subjects to participate in
the research, and to receive extra credit in their class if and only if they emailed the researcher
and specifically asked that their participation in this study be communicated to the faculty
member in question. In this manner, the confidentiality of their participation was preserved
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unless they explicitly asked for it to be revealed to a specific person, in this case the student’s
professor.
3.3.1.2 Interviews The populations that were used to draw interview participants are
somewhat more diverse than those for the tagging and the query activities, which reflects the
shift in focus represented in the research question. The fields drawn from in this portion of the
research were:
Editorial cartoonists: It is reasonable to think that editorial cartoonists might be interested
in the results of this research, as it may change or reinforce the theory and practice of editorial
cartooning, and could help their parent organizations to develop systems to track their editorial
cartoons over time. In this case, it was hoped that the interviews would reveal information about
the experiences of and the lessons learned for the interviewees, so that avenues for future
research might be revealed, and subsequent efforts in this area might more directly benefit from
the work.
Collectors, curators, and librarians: particularly those at museums such as the Ohio State
University Cartoon Library and Museum, and those who run large, revolving collections of
cartoons such as Darryl Cagle at cagle.com, may benefit from this research. Additionally, those
who are responsible for the daily addition of images to large collections, such as image librarians
in academia or in government, were likewise assumed to have an interest in the results of this
work.
3.3.2 Sampling
3.3.2.1 Tagging and query activities The researcher contacted the department chairs
of the various faculties via email where an explanation of what the research is about was offered,
and permission to speak to his or her faculty to recruit participants was sought. The researcher
then contacted each faculty en toto through email, again explaining what the research is about
and what was being asked of them in the study. For these self-selected participants, the time for
cartoon tagging was provided and the website address made available. In the weeks leading up to
the tagging activity, email reminders were sent to participants. When the first phase of data
gathering was complete, access to the cartoons was terminated, and participants were contacted
later for the simulated query phase, after which the subject’s participation was complete.
3.3.2.2 Interviews The interviews were used to ascertain the usefulness and accuracy
of the results from a professional perspective, and used an entirely different sample population
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than did the tagging and query phases. The literature was consulted to find potential starting
points – people who might be willing to be interviewed and who might then provide additional
interested parties for subsequent interviews – for both of the paths of inquiry represented by the
interviews. When potential interviewees were found they were contacted through email to see if
they were both able and willing to be interviewed. If the response was favorable, a time was
arranged and the interview was conducted. At the end of that interview, the participant was asked
if they knew any other people who they believed would also be willing and able to participate,
and such leads were followed until at least three interviews have taken place in both the
cartoonist and the professional tracks.
3.3.3 Description of data gathering environment
This portion of the research was modeled partly after Jörgensen’s 1995 study and partly
after Stvilia and Jörgensen’s 2008 study. Participants were asked to describe editorial cartoons in
a freeform, non-prescribed manner, and in two different contexts, with the assumption, based on
evidence from prior research (see sections 3.2.1 and 3.2.2) that such activities would allow
participants to provide data about what aspects of editorial cartoons should be described in a
system for later retrieval. These images were recent cartoons from noted cartoonists that deal
with issues from the American political scene on the national level. Both activities took place
online using proven, open-source software (steve.tagger, initially created through IMLS grants)
made specifically for enabling users to tag images, and for those tags to be easily collected for
analysis.
3.3.3.1 Images The images used in this study were editorial cartoons from the
following Pulitzer Prize-winning cartoonists: Steve Breen (the 2009 winner), Michael Ramirez
(for 2008), Walt Handelsman (for 2007), Mike Luckovich (for 2006), and Nick Anderson (the
2005 winner). The work of Mark Fiore, the 2010 Prize winner, was not used in this study as his
works are animated, adding a potential layer of description that would not be necessary for the
works of the other authors, constituting a confounding variable. The 2011 Prize winner, Mike
Keefe, had not yet been awarded the Prize at the time the research began.
These cartoonists were chosen because they were assumed to use the best methods for
illustrating their points in the cartoons, and because the praise of their peers was seen to speak to
their effectiveness in covering important issues for their readers. With permission for use
obtained from the copyright holder, cartoons from these authors were included in the study the
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day that they are published online. Neither resolution nor size was changed in any way, though
file format was changed to work with the software being used in this study. See Appendix G for
the cartoons used.
Most of these cartoonists publish three to five times a week. To use their most recent
cartoon in this research would often mean having only one day for a cartoon to be commented
on, a period too short to allow enough people to make enough comments on the cartoon to come
to any conclusions about it. Because of this, the cartoon for each author was updated only once a
week, on Monday, for each of two successive weeks. Because the copyright permissions stem
from the artist’s representatives and not the artists themselves, they should not be influenced in
their work for these weeks by their cartoons’ use in this study because they did not know of the
study taking place.
3.3.3.2 Tagging environment Participants were asked to comment on these cartoons
through the steve.tagger system, a publically-available open-source image tagger initially
developed through a grant from the Institute of Museum and Library Services starting in 2005.
This iteration of steve.tagger, customized for use in this particular study, was hosted by Florida
State University’s College of Communication and Information. The first page encountered on the
website provided an explanation of who was sponsoring the activity, what its purpose was, and
that proceeding to the activity itself constituted consent for the researcher to use the subject’s
tags in research. After this, some basic demographic data was collected: age, gender, level of
education, and political tendencies (conservative, moderate, or liberal). These provided
descriptive data about the participants in an effort to determine if future research might profit
from examining these factors in their samples.
This application then allowed participants to anonymously view a set of five cartoons and
to provide words or phrases that describe those images, but did not allow the participants to see
the tags provided by others. The sole instruction per cartoon was: “Please provide a list of
applicable phrases or words that you think describes this political cartoon.” In this way, it was
hoped that there was as little interference from the researcher as possible when the participants
listed their tags, and that those who might be unfamiliar with tagging in general would still be
able to provide pertinent data. Each participant had up to one week to respond before the next
cycles of images was uploaded, and had the opportunity to edit any of the responses before they
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proceed to the next image. See Appendix H.1 for screenshots of the interface for this portion of
the study.
It was anticipated that participants working in this online environment would complete
the description activity in isolation from other participants, although there was no way to ensure
that this was always the case. Further, little more can be positively said about the conditions
under which this activity took place or the time taken to describe any particular cartoon because
of the distributed nature of the environment, except to say that it took place on the website set up
for the purpose of testing and that the cartoons dealt with recent news events concerning
American politics.
3.3.3.3 Simulated query environment The simulated query activity was assumed at
the outset to produce different frequencies of terms than the tagging activity, and took place three
weeks after the second set of images had been presented for tagging. In this activity, the same
participants that performed the tagging task were asked to create queries that might be used in
their favorite search engine to retrieve the ten cartoons used previously, but were not asked to
execute those queries in any way. This was done using the same program that was used in the
tagging activity, except that the interface was altered to resemble the basic layout of the most
popular search engines. In this, participants viewed the images, then were asked to provide a
query in the text box below the image. This text box provided opportunities to edit their queries
as needed, but did not provide a way to amend a query once it had been submitted. The sole
instruction provided per cartoon for this task asked for whatever key phrases and words the
subject felt was necessary to produce a query that would retrieve the cartoon in question. When
participants had provided queries for all of the cartoons in question, they were specifically told
that the tasks were over, and thanked for their time. See Appendix H.2 for screenshots of the
interface for this portion of the study.
Since the participants had already been informed about the nature of the study and
consent for participation had already been obtained, it was not sought a second time. Likewise,
as pertinent demographic data had already been collected, it was not asked for again. The three-
week period was seen as long enough for the participants to have largely forgotten their previous
responses, thus helping to ensure that this new set of descriptors was not contaminated by those
remembered from the previous set.
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3.3.3.4 Interview environment Following the analysis of the tagging and query
activities, editorial cartoonists and image professionals were interviewed concerning the results.
Before the interviews themselves, the participants were emailed Jörgensen’s 12 Classes with
brief descriptions of each of the Classes and attributes, and asked to arrange them in the order of
importance based on their experiences and assumptions. A time for the actual interview was
arranged via email, the place was wherever the interviewee chose to be for the interview. The
calls were recorded using services provided by recordmycalls.com (2011), which allows phone
calls to be made via the Internet in such a way that the calls can be recorded electronically and
stored securely, thus ensuring that the entire conversation could be replayed as needed to provide
an accurate record of what was said, as well as providing transcripts.
3.3.4 Subject activity
As previously noted, participants were asked to describe editorial cartoons in a freeform
manner and in two different contexts, in the hope that such activities would allow participants to
provide data on what particular aspects of editorial cartoons are important in terms of both
description and retrieval. What follows is a step-by-step description of what each user in the
tagging and simulated query activities was asked to do in the course of this research after
recruitment and up to the end of the data collection effort. This is followed by the activities
undertaken as part of the post-results interview.
3.3.4.1 Informed consent and opting in After recruitment, the participants were
first asked to engage in the tagging portion of this research. This was performed using
steve.tagger, an IMLS-sponsored application which allows Web access to an online environment
for tagging images. The first page available to prospective participants serves to fulfill the
various requirements of informed consent: a general overview, the purpose of the research, the
sequence of events that the participants will be asked to complete, the confidentiality (but not the
anonymity) of their participation, and contact information for both the researcher and the
University’s Institutional Review Board, from whom research approval had been obtained. Also
explicitly stated on this page was that logging into the application – and only logging in –
constituted the provision of informed consent, and that the researcher was free to use the data
provided by the participants in both the research effort and in any resultant publications. At the
end of the page was a link that allowed participants to log in and begin the tagging activity. In
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addition, this consent was also applied to the simulated query activity, as discussed in 3.3.4.4,
discussed later.
3.3.4.2 Demographic information The next page of the tagging activity collected
five pieces of demographic data: age, gender, education, professional affiliation, and political
leaning. These were collected only for later description of the respondents as a group, and to
compare different portions of the population to one another. Since the sampling method was not
random, it would have been inappropriate to perform any sort of regression analysis or to seek
correlations, but it was hoped that future lines of inquiry might be informed by comparing, for
instance, the tagging behavior between genders, or other such comparisons. This was stated on
the webpage that collected this data, so that participants would be able to properly ascertain what
information they would like to provide.
3.3.4.3 Tagging activity Participants were then asked to tag recent editorial cartoons.
Those who started the activity during its first week first tagged five images, then five more the
following week. Those who began the activity during the second week of tagging tagged all ten
cartoons at once. Participants were randomly presented with each of the cartoons to be tagged
that week in thumbnail form, and began with any of the cartoons for that week by clicking on the
appropriate thumbnail. Participants were then presented with a larger version of the thumbnail
image, along with a single line text box for entering whatever tags the participant saw fit to use
in describing the cartoon. Below the text box was a space where already-entered tags could be
seen, so that participants were able to peruse, edit, or delete the tag set before submitting the
answers. When participants were finished with one cartoon and submitted their tags for it,
another randomly selected cartoon was presented and the process repeated. After the last cartoon
was tagged and those tags submitted, a thank you page was displayed, and the tagging portion of
the study was over for that participant.
3.3.3.4 Simulated query activity Three weeks after the second set of five cartoons
had been presented for tagging, each of the participants who tagged images was asked to
describe the same ten images in the context of a simulated query, similar to that which they
might have used with their favorite search engine. For this, the same steve.tagger software used
in the tagging activity was modified to simulate a query environment, imitating the look and feel
of typical contemporary search engine interfaces. It should be noted that this was not a query to
be executed in a search engine, but was instead an effort to gather the queries themselves.
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Participants were asked to log in to this second website, and then to compose queries for each of
the ten cartoons previously used, similar to the tagging activity but within a different
environment. The index page for this phase welcomed the participants back and gave basic
instructions on what followed. Informed consent for both of these activities took place before the
tagging activity, and the demographic data was retained from the former activity as well,
eliminating the need to enter that data a second time.
The query interface itself was modified so that it was noticeably different from the
tagging interface. The main difference in this activity was that where the tagging activity asked
for several tags per image, this query activity asked for only one query per image, although it
was hoped that the query would have several different ideas in it. When all ten images had
queries composed for them, the participants were again thanked for their time, and were
informed that they had finished their participation in this research.
Participants viewed the cartoons in question for a second time, three weeks after having
viewed them the first time. It was anticipated that participants may recall vague details about
some of the cartoons before seeing them again, but that most of the images did not make enough
of an impression to be described in detail before being seen again; Ridgway & Saul (2010) found
similar results when asking native British citizens about the images on the back of a £5 note.
Without research into the shelf-life of editorial cartoons – without knowing how long such
images linger in the minds of readers – it is difficult to ascertain how long a time should be taken
between such tasks to eliminate the effect of recall on a second activity. Add to this the problem
of the images carrying with them a comparatively large set of contextual reminders that could
serve to reorient a reader to the point of a cartoon, and the task of determining how much time
should pass between tagging and querying becomes doubly difficult. While the re-use of
cartoons from the tagging phase may have skewed the results to some small degree, the utility in
comparing the results from the same cartoons in each of these two phases of the research, and the
exploratory nature of this research, was more important, and superseded any minor problems that
manifested as a result of image reuse.
3.3.3.5 Post-results interviews For both cartoonists and image professionals, the
interviews served to explore the degree to which the results from the tagging and the query
activities were expected and in what ways they either support or deviate from traditional notions
in the respective fields. When interviewing cartoonists specifically, the interviews served to
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investigate how the findings might affect the composition of future cartoons, while when
interviewing image professionals, they served to explore how ideas about access to and
preservation of such images might change because of the findings.
Unstructured confirmatory interviews were conducted by telephone in each case; see
Appendix F.3 for the questions from the structured portion of the interview. During the
interview, the order given by the interviewee to Jörgensen’s 12 Classes was discussed, and then
compared to the orders discovered during both the tagging and the query phases of the research.
Interviewees were asked to what degree the results surprised them, and how these findings might
either alter or reinforce their current practices. From there, the interview was allowed to move
freely from one topic to another as seen fit by the interviewee, and ended when it was agreed that
there was no more to be said.
3.4 Data Analysis
In this effort, post-positivism helped direct the work by focusing the analysis of both the
tags and the simulated queries for editorial cartoons on the participant, on their point-of-view
concerning the images in question, and on what they think are the most important aspects of
those images when addressing concerns in representation. The imposition of pre-determined
requirements for the description of these images was inappropriate; as an exploratory work, the
ideas and points made by the participants are more important than determining what is the most
applicable metadata schema or descriptive system for use in retrieval. As this may serve as the
foundational work for subsequent studies, the determination of the realities for the participants –
as determined by their tags and descriptions – is both crucial and warranted.
3.4.1 Tagging Activity
3.4.1.1 Tag analysis For this analysis, the sampling unit was the individual cartoon, as
they were selected exemplars of the kind of image that is the focus of this research. The coding
unit was the tag, which is defined for this research as either a word or a phrase offered as a
partial descriptor of a given editorial cartoon, regardless of the environment which produced the
tag. Rather than draw from every editorial cartoon ever published, this research instead used a
purposive sampling method, drawing from recent cartoons by the five most recent Pulitzer Prize
winning editorial cartoonists who produce standard, still images (thus excluding animated
editorial cartoons). It was assumed that such cartoonists were able to produce the best quality
cartoons which would be the least confusing and most communicative available.
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Tags were placed into one of Jörgensen’s 12 Classes. The 12 Classes are: LITERAL
OBJECT, COLOR, PEOPLE, LOCATION, CONTENT/STORY, VISUAL ELEMENTS, DESCRIPTION, PEOPLE
QUALITIES, ART HISTORICAL INFORMATION, PERSONAL REACTION, EXTERNAL RELATION, and
ABSTRACT CONCEPTS; see Appendix D for a full listing oerewhat each Class entails and the
subordinate attributes within each Class. These were developed using book illustrations as the
images to be described. Singular and plural forms will generally be treated as one and the same,
unless otherwise dictated by the nature of the tags themselves. Likewise, misspellings were noted
and, when possible, corrected so that similar tags may be more easily grouped together. When
appropriate, multi-word phrases were considered one term, and were placed with the appropriate
Class or Classes just as if they were a one-word descriptor. When a tag was judged to be in some
degree of error, but the error seemed reasonable, it was placed in an appropriate class as if it was
correct.
Tags were placed into a Class or Classes in an iterative process. First, tags were placed in
each appropriate Class that had even the least chance of being applicable; some tags were
initially placed in up to four Classes. The next iteration examined the attributes within each Class
as they might apply to the tag in question, then each tag/Class pairing was examined for
appropriateness and compared to the other pairings to determine which should be kept and which
were, upon examination, not considered proper descriptions of those tags. When a tag was found
to have a complete and appropriate description for being placed in a Class, the consideration
ended. When a tag was found to have two or more seemingly appropriate tag/Class pairings that
might reasonably be used to describe it, those were examined further to see if the inclusion of all
the descriptors added to the record of the image, or only served to clutter the record through
repetition and redundancy. If a tag was found to be both beneficial and appropriate in two or
more Classes, all Classes were in fact applied, a so-called “double coding” (or more, when
applicable).
One particular type of tag was discovered in the course of this research that did not
reasonably fit into any existing Classes, that type being a tag that had no discernible connection
to the image in question. It was found that the inclusion of such tags in an extant Class would
serve only to muddy the waters by inflating the importance of one Class at the expense of others.
Thus, when a tag was judged to be in such a high degree of error that it would skew results
because of the high likelihood of putting it in the wrong Class, it was counted as a Wayward
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Term Failure, meaning the term failed to describe any meaningful aspect of the image in
question. These were held out from the standard 12 Classes so that they might shed some light on
tagging as a practice, without misrepresenting the legitimate results of describing the cartoons.
The codebooks were set up in a uniform manner, consisting of 6 sheets (called “plies”)
per spreadsheet. One ply held the raw data for each cartoon in each of the tagging and simulated
query environments, including all the demographic data collected for the participants on a per tag
basis. The next ply had just the tags themselves in a column, divided appropriately when a given
tag held more than one item to be coded. Across the top of this ply were columns representing
Jörgensen’s 12 Classes. Each of these column headers had a note attached to it, which could be
accessed by rolling over it with the cursor. This note was a verbatim explanation from Jörgensen
(1995) that explained what is meant by each class and the concomitant attributes for each Class,
ensuring that quick and accurate placement for each tag would take place. Individual tags were
given unique numeric identifiers at this point. If it was found that a given tag might fit into a
given Class, an “X” was placed in that cell which represented the appropriate tag/Class pairing.
The application of Classes to tags was, in this particular iteration of the coding, as liberal as
could be reasonably justified. The next ply refined these results, applying more stringently and
rigorously the attributes within each class when determining the appropriateness of the Classes.
When an initial Class was found to be incorrect for a given tag, that Class was removed. Where it
was found to be correct, further coding took place, noting which of the attributes within that tag
was most applicable. When this was complete, the information was placed in another ply, where
the matching of demographic data to the newly-coded tags took place, and where a final
comparison of tagging practices within that cartoon and among the tags themselves occurred,
ensuring consistency of practice within a given cartoon. The other two plies per cartoon
contained the cartoon itself (for comparison and clarification purposes), and a working sheet,
where totals and other results on a per cartoon basis could be assembled and calculated.
The tags for each cartoon were analyzed in alphabetical order of the participant’s last
name for each of the two weeks the images were drawn from. When the ten cartoons had their
tags labeled by Class and attribute, the analysis was set aside for one week, then reviewed again,
and mistakes from the first analysis were corrected. This process was repeated a third time, and
fourth time, the latter of which found no mistakes, which concluded the iterative analysis of the
tags.
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3.4.1.1.1 Review of practice – tags. The non-random nature of this study precludes
the use of Krippendorf’s methods of validation. But allowing the work of the researcher to go
completely unchecked is contraindicated on its face. As formal intercoder reliability measures
were inappropriate, a less formal review of the outcomes of this research – a simple review
where the focus was on following the definitions of the Classes and attributes properly was
paramount – was in order. To this end, an outside reviewer was engaged to review the tags and
queries for two different cartoons by two different authors. This reviewer, a doctoral student
from a library and information studies program in the southeastern United States, reviewed two
randomly chosen cartoons from nine possibilities (one cartoon was used to establish the
correctness of the use of the Classes by other means, and as such it was inappropriate to review it
again). This reviewer performed the review after a period of instruction by the researcher, and
was given anonymised data in codebook form from both phases of the study for each of two
cartoons.
3.4.1.2 Tag comparison After the analysis of tags was completed, comparisons
between the various demographic variables took place: male responses were compared to female
responses, degree holders to non-degree holders, and among the three general political paradigms
(conservative, moderate, and liberal). In these, differences in tagging behavior were scrutinized
and commented on when deemed appropriate.
When leaving demographic variables aside and considering the whole of the tags for
these cartoons, two sets of comparisons took place. First, tag frequency among cartoons was
analyzed, where the number of tags in each Class and the overall percentage of description that
each tag represents for each image was compared. The specific effort here was to determine if,
for instance, the LITERAL OBJECT Class was used at a steady rate for each of the editorial
cartoons. While it was anticipated that the cartoons’ tag sets will show a great deal of difference
in the frequency with which the Classes would occur, a comparison was necessary to discover if
this were true.
The second comparison involved the totals for each class from the entire set of images
and similar totals from other studies that used tagging as the basis for the description of a set of
images. Laine-Hernandez and Westman (2006) asked 10 participants to provide keywords for 40
newspaper images, and another 10 to describe the same images in an unconstrained environment.
These results were then parsed into Jörgensen’s 12 Classes and were found to be a different
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proportion of tags than found in Jörgensen’s work. The work of Brunskill & Jörgensen (2002)
was also included for comparison, where they performed research along a similar model but
using illustrations, graphs, and charts instead of newspaper images. Jörgensen’s work from 1995
was included, where she used randomly selected images from a notable illustrations collection as
part of a design that asked participants to describe such images free of any template or other
direction for the task, making three sets of images that use Jörgensen’s Classes to describe their
tags with which to compare the results of this research.
3.4.2 Simulated query Activity
3.4.2.1 Query Analysis The analysis of the simulated query results was different from
that performed on the tags because of the nature of the way that the two present themselves. Tags
are largely self-delimiting; intended breaks in ideas and phrases were represented by CRLF-type
hard returns used by the participant. In the simulated query environment, participant’s responses
to the editorial cartoons were usually one line, non-delimited responses, forcing the investigator
to make some assumptions about where phrases within the line began and ended. In some cases,
participants included punctuation to indicate where breaks in phrases occurred, but in most cases,
ad hoc determinations for parsing queries into key phrases or words based on the content of the
query itself were made. Where indeterminacy was found, phrases from the queries were kept
together, and the analyzed portion of the phrase set apart in brackets. For instance, the phrase
“deficit committee thanksgiving turkey cartoon” was parsed into four phrases: “deficit
committee,” thanksgiving,” “turkey,” and “cartoon,” each with its own set of Classes and
attributes, but in each case, the phrase being Classed and attributed was couched in the context of
the entire string of descriptors. See Appendix J.
Once this was done for each participant’s response, the analysis closely mirrored that
done in the tagging portion of this research. The sampling unit was the individual cartoon and the
coding unit was the words and phrases derived from the queries. The codebook was in an
identical Excel spreadsheet, and the same comparisons by demographic variables were made.
3.4.2.1.1 Review of practice—queries. As noted, a reviewer was engaged to
evaluate the propriety of the classification of the tags in Section 3.4.1.1.1. An identical
arrangement with the same reviewer was made pertaining to the query phase of this study, and
her work was performed on the same two cartoons.
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3.4.2.2 Word and phrase comparison As before, two comparisons took place, the
first focused on the different frequencies of Jörgensen’s 12 Classes within the simulated query
set of cartoons, the other on how the set as a whole compares to other work that parsed query
terms into the 12 Classes. In the first, the frequency of Classes as represented by key phrases and
words were compared among cartoons, and differences noted. Great differences in key phrase
and word type frequencies were not found, but those differences that were apparent were
commented on. Every effort was made to make this portion of the simulated query comparison as
much like the tag comparison as possible.
The second part of the simulated query comparison involved comparing the composite
frequency of phrases and words derived from the simulated queries found for this set of images
to other results from similar studies that analyzed image queries and used Jörgensen’s 12 Classes
to describe the results. Jansen (2007) used the 12 Classes, among others, to map image queries
made in a major Web search engine, finding that web-based queries had a large number of image
attributes that did not sit well in the 12 Classes, such as cost, URL, and image collection name,
but these are excluded from use in the research at hand (Jörgensen specifically excluded these as
“bibliographic” data, not “image content” data). He also found somewhat different proportions
among the classes. Chen (2000) analyzed the queries of 29 art history students in their
completion of an assignment for class. He found significant differences between the frequency of
the Classes in his study and that of Jörgensen. Jörgensen’s 1995 work includes a query analysis
of six images presented to naïve participants who were asked to imagine their ideal retrieval
system and to provide their query in such a system for each image.
3.4.3 Tag-simulated query comparison
After the simulated query portion of the research was analyzed, the results were
compared to those found in the tagging activity. This comparison was intended to show which of
Jörgensen’s 12 Classes are most important to the description of editorial cartoons; when a
particular Class of image description is often found in both tasks, it was speculated that the Class
in question would be profitably used in describing editorial cartoons, enough so that more time
and effort should be put into this particular Class when describing large collections of cartoons.
3.4.4 Interview analysis
The analysis of unstructured confirmatory interview data was steered toward three
general areas of inquiry. First, might the findings of the tagging and the query activities have any
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bearing on either the community of editorial cartoonists or the more general community of image
professionals? Data pertaining to this was found in the comparison of the participants anticipated
order of frequency for Jörgensen’s 12 Classes and the order found in the results of the research,
and the degree to which each of the interviewees might change their perceptions and practices
based on this. The second area of inquiry was based on the first: how might the aforementioned
changes take place, and what new research needs to be done to further solidify those changes?
Data for this was expected to be in the form of the follow-on discussions that might take place
after the previous questions was discussed. Other data that presented itself during the interviews
that is of interest to the researcher or the field of information studies’ interests was gathered and
discussed as well.
3.5 Validity and Reliability
3.5.1 Validity
Content analysis in general produces a high degree of validity because of the lack of
artificial or intermediate steps or actions between the creation of the phenomenon sought for the
research and the analysis of that data. Face validity is assumed because of the aforementioned
number of similar research methods that have been accepted into the literature by peer review.
Social validity is assumed based on the benefactors of this research as described in section 1.5
(the cartoonists themselves, educators, and the community of library and information studies,
including users). While these simple and straightforward measures of validity are met in this
research, there are a number of other forms of validity in content analysis that Krippendorff
(2004) describes, some of which apply to this work and others that do not.
Krippendorff describes semantic validity in content analysis as a kind of content validity,
stating that this measure evaluates “the degree to which analytical categories accurately describe
meanings and uses in the chosen context” (p. 319). This type of validity is assured in this
research because of the number of previous studies, noted at length in 3.4.1.2 and 3.4.2.2, that
have used Jörgensen’s 12 Classes to do what is being done in this study, albeit with different
kinds of images.
He also describes two types of internal structure validity: structural and functional.
Structural validity deals with “whether the analytical constructs [that content analysts] have
adopted accurately represent the known uses of the available texts, the stable meanings, language
habits, signifying practices, and behaviors in the chosen context” (p. 330). In this research, the
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text in question is the tags and queries generated by the participants in response to the cartoon
images they are shown, and the analytical construct is Jörgensen’s 12 Classes. We know that this
match of text to construct is valid because the Classes have stable meanings, and the language
habits and signifying practices exhibited by taggers and queriers when dealing with images are
well-established. The use of the 12 Classes with the tags and queries can thus be said to have
structural validity as described by Krippendorff.
Functional validity is “the degree to which analytical structures are vindicated in use
rather than in structure” (p. 332). It has been established that the analytical structure (the 12
Classes) have been used in image research before, both in the analysis of tags and the analysis of
queries, showing itself to be both useful and successful in describing the types of image
description commonly given by the participants in such studies, and thus showing the functional
validity of using the 12 Classes in this research.
Krippendorf’s description of sampling validity shows that the research at hand is not
valid in this way for the images themselves. The images were chosen based on their being
produced by Pulitzer Prize-winning artists because of the assumed expertise in visually
commenting on national issues that the award represents, and because it was hoped that such
images would produce the most tags and query terms. But in doing so, it was possible that
cartoons of equal quality that did not win the Pulitzer Prize were excluded, as was work from
less-recognized artists, leading to a possible skew in the results. Krippendorff’s correlative
validity did not apply to this work because the frequency of use for each of the 12 Classes was
markedly different than those found in similar studies, and predictive validity did not apply as
the sampling method is not random.
3.5.2 Reliability
In contrast, the degree of reliability is in question, mainly because the nature of the
research is such that only one researcher analyzed the data, raising the possibility that personal
bias in analysis and one-time mistakes in coding went uncorrected. Steps to counter these
concerns have already been listed: both previous training and experience with Jörgensen’s 12
Classes, and the inclusion of definitions of the 12 Classes in the codebook itself for easy and
ready reference are measures to help ensure the reliability of the results of this research.
Krippendorf’s α cannot be appropriately used in this content analysis because of the lack of a
second, independent researcher to perform the slotting of the tags and terms into Jörgensen’s 12
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Classes, among other reasons. In order for this measure of reliability to be used, at least one other
researcher would have to analyze the entire dataset; using a sample of the dataset would be
improper (Krippendorff, 2004).
3.6 Limitations
Panofsky's theory of iconology (discussed in 2.2.1) is limited to the identification of the
constituent parts of the image and to the description of an overarching theme or topic on which
the image might comment. Other aspects of images are not considered in this theory, but may be
used as key phrases or words when describing them. For instance, metadata elements can be
grouped into descriptive, structural, and administrative data, but Panofsky only has a place for
the descriptive elements and does not consider the other two. Similarly, Jörgensen’s 12 Classes
of image description concentrate on describing efforts to turn the story of the image into words,
choosing to allow for only the briefest descriptions of what might be termed “bibliographic
concerns.” The structure and composition of the Classes allowed for a minimal representation of
image description besides that which dealt with the story told by the image.
The authors of the editorial cartoons chosen for this research created their images for
print, i.e. their work is intended for print media, but is easily adapted to electronic and Web-
based applications. The 2010 winner of the Pulitzer Prize for editorial cartooning, Mark Fiore,
publishes only for the web version of the San Francisco Chronicle and takes advantage of that
medium to create animated editorial cartoons. These kinds of cartoons were not used in this
study as they may have introduced an element to description that cannot be found in for-print
cartoonists’ work, though they may be a topic for future research.
The electronic environment in which this data was collected limited the kinds of
responses being collected to those of text; there was not an opportunity for the users to
graphically represent their ideas or illustrate relationships between constituent parts of an
editorial cartoon (as they might do in a paper-based study, a possibility provided for in
Jörgensen’s 12 Classes), neither was there a chance to discuss a given image verbally in real time
with the researcher as part of the data collection (as there would be in an interview or face-to-
face experiment). Additionally, the remote, electronic environment may have produced
somewhat different results than a face-to-face, pen-and-paper environment would have; some of
the studies whose results are used for comparison did use an in-person method for collecting
data, while others used a Web-based electronic format like the one here.
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All of the participants for the tagging phase were invited to participate in the query phase
as well. Half of those invited did so, in this specific order. This may be a limitation for this study
in that allowing some of the participants to perform the query task before the tagging task – the
opposite of what was done here – may have garnered different results. While this cannot be
known for certain, it is something that should be considered in future efforts. Additionally, using
the same participants in both phases may have produced a halo effect between the two; responses
generated while tagging may have manifested during query generation. Again, this is not certain,
but the three weeks that passed between the first phase and the second was deemed long enough
for such effects to be minimized.
The instructions given to the participants in the tagging and query phases were
intentionally vague to allow the user to do as he saw fit; efforts were made to limit the intrusion
of the instruction into the participant’s views and practices so that the clearest, most pure tags
and queries would be produced. It is possible that the ambiguity of the instructions led to some
level of confusion in some participants, as they may have been unsure of what was being asked
for or what was expected, even though the point of the research and what was being asked of
them was made clear in several places, namely the informed consent page and the introduction
page.
Only the researcher generated the results in this work, mostly because of the nature of a
dissertation and its place in the development of academic researchers. The results of this research
stemmed from the efforts of a sole researcher in both the division of tags and queries into
discrete units for analysis, and from the same researcher classifying those units using Jörgensen’s
12 Classes. While a preliminary review of tags and queries for one cartoon was conducted by the
originator of the 12 Classes, and a similar review of two other cartoons was conducted by
another associate, nothing approaching full intercoder reliability measures was employed in
reviewing the categorization of the tags and queries in this work. As such, the researcher’s biases
may be present in the final work, and the results may be skewed by unchecked overfamiliarity
with the material.
Both populations in this blended sample for the first two phases self-selected their
participation. The academics that participated weren’t targeted for recruitment on an individual
basis; rather, entire and specific departments were targeted, and the participants self-selected
from within those departments. The students that participated self-selected out of self- interest,
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namely to get extra credit in a class. In both cases, no semblance of random selection was
present, so the generalizability of the results to the population at large is limited.
Some of the interviewees for the third phase of the study were specifically targeted based
on several different factors. For the professionals, most had crossed paths on a social or
professional basis with the researcher, but were not in any way involved in similar research or
academic activities. Other professionals were recruited on the suggestion of previous
interviewees. Cartoonists were recruited at the suggestion of different professional associations,
and those artists then suggested both other cartoonists and professionals in the field of image
management for subsequent interviews. Again, random sampling was not used, limiting the
utility of the results to similar populations.
3.7 Ethical and legal concerns
3.7.1 Ethical concerns
The ethical concerns for this research are minimal. Anonymity was neither guaranteed
nor sought because the ability of the researcher to contact participants for the simulated query
activity was essential. But confidentiality was maintained (within the bounds of the law), and the
disposal of the records will conform to research norms when the time comes. In terms of the data
collected about the participants, the ethical concerns attached to this research are both
manageable and negligible. The research did not proceed until approved by the Institutional
Review Board of the same research-oriented university in the southeastern United States that
hosted the software used.
Likewise, worries about the effect of performing the tasks put forth in this research were
small. Evidence in the literature shows that editorial cartoons are sometimes designed to elicit an
emotional response, particularly through using humor, anger, or sadness. The researcher
regarded it as possible – but not likely – that viewing the images used in this study may cause
either raucous laughter (which may have been regarded as embarrassing for the participant),
remarkable sadness (which may have affected subsequent work efforts by the participants
themselves), or anger (with similar results to sadness). In any case, the possibility of such events
was addressed in the informed consent portion of the website, before any participation was
sought.
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3.7.2 Legal concerns
The legal concerns in this study centered on the proper acquisition of permission to use
copyrighted materials, but turned out to be unfounded: uClick.com provides blanket permission
for all of its images and content to be used in academic research, specifically including
dissertations. The IMLS underwrote the creation of the steve.tagger software, and has granted
open access to the source code and to the finished product, provided that acknowledgement of
the organization is made. In this research, the software has undergone minor revision, enough so
that keeping the native language stating that the IMLS had the software created at its behest is no
longer operative. Nevertheless, an acknowledgement of the origin of the software is retained and
thanks given, fulfilling the obligation owed.
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CHAPTER 4
RESULTS
4.1 Tagging phase
4.1.1 Participants
51 total participants provided data for this study. Of those, 13 self-identified as
conservative in their political leanings, 22 as Moderate, and 16 as Liberal. 19 of the participants
hold some sort of degree (Bachelor’s or above) and 32 did not. 29 participants are female, and 22
are male. Cartoons were posted on October 31 and November 7, 2011. The 51 participants
tagged between five and ten of the ten cartoons posted by the end date of Phase 1, November 13.
These cartoons can be found in Appendix G.
4.1.2 Tagging results
In the tagging phase, participants left a total of 1533 attributes for the ten cartoons.
Table 4
Summary data for the tagging phase image name # of participants # of tags avg. # of tags/participant
ande1 43 175 4.27
bree1 43 155 3.78
hand1 44 166 4.05
luck1 43 160 3.90
rami1 43 159 3.88
ande2 39 144 3.89
bree2 40 165 4.34
hand2 39 143 3.86
luck2 39 136 3.68
rami2 39 130 3.51
Note: Not all 51 participants tagged each cartoon.
The images from the first week of the tagging phase are listed first and end with “1,”
while the images from the second week end in “2”. Three participants only contributed to the
first week’s cartoons, and two other participants only contributed to one cartoon out of the ten.
While several more participants contributed during the second week, commenting on all ten
images at once, this was not enough to produce similar numbers of attributes for the second
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week. However, the average number of attributes per tagger for the first week was 3.98, while it
was 3.86 for the second week, a small reduction.
4.1.3 Results – Tagging Phase
4.1.3.1 – Image “ande1”
Figure 1 andi1 [in color] (Anderson, 2011b)
Table 5
Classes and attributes for”ande1” – tagging environment
Classes frequency of
Classes % of total
attributes frequency of attributes
% of total
Abstract Concepts 75 42.9
(abstract) 14 8.0
(atmosphere) 15 8.6
(theme) 46 26.3
(symbolic aspect) 0 0
Literal Objects 34 19.4 (object) 2 1.1
(text) 32 18.3
Viewer Reaction 22 12.6
(conjecture) 0 0
(personal reaction) 20 11.4
(uncertainty) 2 1.1
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Table 5 - continued
Classes frequency of
Classes % of total
attributes frequency of attributes
% of total
People-Related Attributes
17 9.7 (emotion) 0 0
(social status) 17 9.7
People 12 6.9 (people) 0 0
(PEOPLE) 12 6.9
Content/Story 8 4.6
(activity) 1 0.6
(category) 1 0.6
(event) 0 0
(setting) 4 2.3
(time aspect) 2 1.1
External Relation 0 0 (reference) 0 0
(similarity) 0 0
Art Historical Information
2 1.1
(ART HISTORICAL INFORMATION) 1 0.6
(artist) 1 0.6
(format) 0 0
(technique) 0 0
(time reference) 0 0
Wayward Term Failure 5 2.9 (WAYWARD TERM FAILURE) 5 2.9
Description 0 0 (description) 0 0
TOTAL 175 100.1 TOTAL 175 100.1
Note: N = 43. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
The ABSTRACT CONCEPTS class was dominated by the attribute Theme. The LITERAL
OBJECTS class likewise mostly made references to the attribute Text within the image. Most of
the VIEWER REACTIONS were Personal Responses, and many of the tags under PEOPLE-RELATED
ATTRIBUTES dealt with the attribute Social Status, usually to speak about a political party or a
school of political thought. This image had no depiction of any person within it, but still referred
to a specific person (President Obama, in this case), thus necessitating the use of the Class in
place of an Attribute as dictated by Jörgensen’s rules. Similarly, one participant noted the name
of the newspaper that originally published the cartoon and, there being no specific place for such
information in the original 12 Classes, it was placed in the Class ART HISTORICAL INFORMATION
as it clearly the place for such thing, even without a specified Attribute to accompany it. All of
the WAYWARD TERM FAILUREs had to do with what had happened to the plane in the cartoon,
stating it had been shot down or was burning, which is clearly not the case.
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4.1.3.2 – image “bree1”
Figure 2 bree1 [in color] (Breen, 2011b)
Table 6
Classes and attributes for”bree1” – tagging environment
Classes frequency of
Classes % of
Classes attributes
frequency of attributes
% of Attributes
Abstract Concepts 76 45.8
(abstract) 6 3.6
(atmosphere) 16 9.6
(theme) 54 32.5
(symbolic aspect) 0 0
Literal Objects 34 20.5 (object) 0 0
(text) 34 20.5
Viewer Reaction 23 13.9
(conjecture) 2 1.2
(personal reaction) 19 11.4
(uncertainty) 2 1.2
People-Related Attributes
8 4.8 (emotion) 4 2.4
(social status) 4 2.4
People 4 2.4 (people) 4 2.4
(PEOPLE) 0 0
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Table 6 - continued
Classes frequency of
Classes % of
Classes attributes
frequency of attributes
% of Attributes
Content/Story 12 7.2
(activity) 0 0
(category) 1 0.6
(event) 11 6.6
(setting) 0 0
(time aspect) 0 0
External Relation 4 2.4 (reference) 4 2.4
(similarity) 0 0
Art Historical Information
2 1.2
(ART HISTORICAL INFORMATION) 1 0.6
(artist) 1 0.6
(format) 0 0
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
3 1.8 (WAYWARD TERM FAILURE) 3 1.8
Description 0 0 (description) 0 0
TOTAL 166 100 TOTAL 166 99.8
Note: N = 43. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order.
While ABSTRACT CONCEPTS are still the most often-used Class for describing this
cartoon, this is the only instance in which LITERAL OBJECTS approaches the same proportion of
use, with the attribute Text being the most frequent. This is also the cartoon with the lowest
proportion of VIEWER REACTIONS, the image with the highest use of CONTENT/STORY, and was
the only cartoon to elicit a reference to another cartoon (using the attribute Similarity) in the
tagging phase, noting that this cartoon bore some similarity to Gary Larson’s The Far Side.
4.1.3.3 – image “hand1” That the Object class produced nothing but references to text
is unsurprising, for two reasons: first, there are very few objects within the cartoon to name, as
they seem to be props to help identify the PEOPLE in the cartoon as high school students; and
second, there is an unusually large amount of text in this image for participants to refer to. What
makes these references to Text odd is that a number of the participants seemed to miss the large
“SAT Testing” label in the background, and as a result seemed to think that the cartoon was
about airport security in general, rather than about a recent SAT testing scandal. While these led
to inappropriate tags vis-à-vis the image when they dealt with the specific, both airport security
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and newly-implemented security measures at some SAT testing sites were, when spoken to
generally, sufficiently alike that they were both included in the general body of tags.
Figure 3 hand1 [in color] (Handelsman, 2011b)
Table 7
Classes and attributes for”hand1” – tagging environment
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of Attributes
Abstract Concepts 76 45.8
(abstract) 6 3.6
(atmosphere) 16 9.6
(theme) 54 32.5
(symbolic aspect) 0 0
Literal Objects 34 20.5 (object) 0 0
(text) 34 20.5
Viewer Reaction 23 13.9
(conjecture) 2 1.2
(personal reaction) 19 11.4
(uncertainty) 2 1.2
People-Related Attributes
8 4.8 (emotion) 4 2.4
(social status) 4 2.4
People 4 2.4 (people) 4 2.4
(PEOPLE) 0 0
Content/Story 12 7.2
(activity) 0 0
(category) 1 0.6
(event) 11 6.6
(setting) 0 0
(time aspect) 0 0
External Relation 4 2.4 (reference) 4 2.4
(similarity) 0 0
101
Table 7 - continued
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of Attributes
Art Historical Information
2 1.2
(ART HISTORICAL INFORMATION) 1 0.6
(artist) 1 0.6
(format) 0 0
(technique) 0 0
(time reference) 0 0
Wayward Term Failure 3 1.8 (WAYWARD TERM FAILURE) 3 1.8
Description 0 0 (description) 0 0
TOTAL 166 100 TOTAL 166 99.8
Note: N=44. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
4.1.3.4 – image “luck1”
Figure 4 luck1 [in color] (Luckovich, 2011b). In the banner, the words “mission” and
“accomplished” are in yellow, whiel the other words are in white.
102
Table 8
Classes and attributes for”luck1” – tagging environment
Classes frequency of Classes
% of classes
attributes frequency of attributes
% of attributes
Abstract Concepts 47 29.4
(abstract) 6 3.8
(atmosphere) 4 2.5
(theme) 37 23.1
(symbolic aspect) 0 0
Literal Objects 35 21.9 (object) 6 3.8
(text) 29 18.1
Viewer Reaction 23 14.4
(conjecture) 0 0
(personal reaction) 22 13.8
(uncertainty) 1 0.6
People-Related Attributes
5 3.1 (emotion) 4 2.5
(social status) 1 0.6
People 33 20.6 (people) 31 19.4
(PEOPLE) 2 1.3
Content/Story 3 1.9
(activity) 2 1.3
(category) 1 0.6
(event) 0 0
(setting) 0 0
(time aspect) 0 0
External Relation 9 5.6 (reference) 9 5.6
(similarity) 0 0
Art Historical Information
4 2.5
(ART HISTORICAL INFORMATION) 1 0.6
(artist) 1 0.6
(format) 2 1.3
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 0.6 (WAYWARD TERM FAILURE) 1 0.6
Description 0 0 (description) 0 0
TOTAL 160 100 TOTAL 160 100.1
Note: N = 43. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
The text in this image presented an unusual challenge. On the one hand, the words
“mission” and “accomplished” are never found in succession, yet the image clearly alluded to the
“Mission Accomplished” banner behind then President Bush during his 2003 speech on the
aircraft carrier USS Abraham Lincoln. The coloring of those particular words in the image made
their connection more explicit, but since they were not in direct proximity, their placement into
specific classes was a unique problem in this dataset. It was decided that the words both
103
constituted an EXTERNAL RELATION to a previous event, and constituted a statement of the
Theme of the image. This image is also the only one used in this research to show more instances
of emotion than of social status, referring to the emotion thought to be experienced by President
Bush in the image.
4.1.3.5 – image “rame1”
Figure 5 rame1 [in color] (Ramirez, 2011b)
Table 9
Classes and attributes for”rame1” – tagging environment
Classes frequency of Classes
% of Classes
attributes frequency of
attributes % of
attributes
Abstract Concepts
59 37.1
(abstract) 13 8.2
(atmosphere) 10 6.3
(theme) 36 22.6
(symbolic aspect) 0 0
Literal Objects 36 22.6 (object) 12 7.5
(text) 24 15.1
Viewer Reaction 23 14.5
(conjecture) 0 0
(personal reaction) 23 14.5
(uncertainty) 0 0
People-Related Attributes
9 5.7 (emotion) 1 0.6
(social status) 8 5
104
Table 9 - Continued
Classes frequency of Classes
% of Classes
attributes frequency of
attributes % of
attributes
People 8 5.0 (people) 0 0
(PEOPLE) 8 5
Content/Story 12 7.5
(activity) 1 0.6
(category) 0 0
(event) 2 1.3
(setting) 9 5.7
(time aspect) 0 0
External Relation 3 1.9 (reference) 3 1.9
(similarity) 0 0
Art Historical Information
2 1.3
(ART HISTORICAL INFORMATION) 1 0.6
(artist) 0 0
(format) 1 0.6
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
7 4.4 (WAYWARD TERM FAILURE) 7 4.4
Description 0 0 (description) 0 0
TOTAL 159 100 TOTAL 159 99.9
Note: N = 43. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
The sole object named in this image – either in the generic (plane) or in the specific (Air
Force One) – also served as the setting for the image, resulting in an unusually high number of
attributes for the number of raw (unparsed) tags. Also unusual is the high number of PEOPLE
named in association with the image (the highest proportion of use for this Class), while there are
no PEOPLE actually depicted in it. This cartoon represents the largest number of WAYWARD TERM
FAILUREs, with five. Of those, five are the letters “USA,” which occur nowhere in the image but
may refer to the words on the side of the plane. The other of those terms is “Al Gore” and “Spirit
Airlines,” for which no realistic connection can be made to this image.
4.1.3.6 – image “ande2” This cartoon produced an unusually high proportion of
objects named when compared to the text noted, those objects centering on the turkey but also
mentioning the axes, chopping block, and party symbols. This image also found the highest
number of references to the Time Aspect attribute of the image – Thanksgiving – which helped
to complete the setting both in time and in tradition. While no PEOPLE are depicted in the image,
105
“Pilgrims” were noted to some degree in connection with it, connected directly to the time aspect
of the image.
Figure 6 ande2 [in color] (Anderson, 2011c)
Table 10
Classes and attributes for”ande2” – tagging environment
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts 62 43.1
(abstract) 3 2.1
(atmosphere) 11 7.6
(theme) 48 33.3
(symbolic aspect) 0 0
Literal Objects 27 18.8 (object) 10 6.9
(text) 17 11.8
Viewer Reaction 22 15.3
(conjecture) 0 0
(personal reaction) 22 15.3
(uncertainty) 0 0
People-Related Attributes
18 12.5 (emotion) 0 0
(social status) 18 12.5
People 3 2.1 (people) 2 1.4
(PEOPLE) 1 0.7
Content/Story 7 4.9
(activity) 1 0.7
(category) 0 0
(event) 0 0
106
Table 10 - continued
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Content/Story (cont.)
(setting) 0 0
(time aspect) 6 4.2
External Relation 0 0 (reference) 0 0
(similarity) 0 0
Art Historical Information
4 2.8
(ART HISTORICAL INFORMATION) 1 0.7
(artist) 1 0.7
(format) 2 1.4
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 0.7 (WAYWARD TERM FAILURE) 1 0.7
Description 0 0 (description) 0 0
TOTAL 144 100.2 TOTAL 144 100
Note: N = 39. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
4.1.3.7 – image “bree2”
Figure 7 bree2 [in black & white] (Breen, 2011c)
107
Table 11
Classes and attributes for”bree2” – tagging environment
Classes frequency of
Classes % of
Classes attributes
frequency of attributes
% of attributes
Abstract Concepts 55 33.3
(abstract) 7 4.2
(atmosphere) 0 0
(theme) 48 29.1
(symbolic aspect) 0 0
Literal Objects 40 24.2 (object) 17 10.3
(text) 23 13.9
Viewer Reaction 23 13.9
(conjecture) 1 0.6
(personal reaction) 19 11.5
(uncertainty) 3 1.8
People-Related Attributes
18 10.9 (emotion) 1 0.6
(social status) 17 10.3
People 22 13.3 (people) 22 13.3
(PEOPLE) 0 0
Content/Story 2 1.2
(activity) 1 0.6
(category) 0 0
(event) 1 0.6
(setting) 0 0
(time aspect) 0 0
External Relation 2 1.2 (reference) 2 1.2
(similarity) 0 0
Art Historical Information
3 1.8
(ART HISTORICAL INFORMATION) 1 0.6
(artist) 1 0.6
(format) 0 0
(technique) 0 0
(time reference) 1 0.6
Wayward Term Failure
0 0 (WAYWARD TERM FAILURE) 0 0
Description 0 0 (description) 0 0
TOTAL 165 99.8 TOTAL 165 99.8
Note: N = 40.Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
The Personal Responses for the cartoon were more personal than those found for other
images; where others tended to garner interpretations of what the image meant or was speaking
to, this image brought out mostly statements of agreement or disagreement or of supplementary
editorializing or the addition of personal comment in connection with the image. This personal
identification with the issues in the image continued in the naming of the PEOPLE within the
image: some called the central character a “protester,” while others called him a “dirty hippie,”
108
and other such polarizing categorizations. This is also the only image with no Wayward Terms
Failures in the tagging phase of the research.
4.1.3.8 – image “hand2”
Figure 8 hand2 [in color] (Handelsman, 2001c)
Table 12
Classes and attributes for”hand2” – tagging environment
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts 58 40.6
(abstract) 18 12.6
(atmosphere) 4 2.8
(theme) 34 23.8
(symbolic aspect) 2 1.4
Literal Objects 22 15.4 (object) 2 1.4
(text) 20 14
Viewer Reaction 27 18.9
(conjecture) 0 0
(personal reaction) 27 18.9
(uncertainty) 0 0
People-Related Attributes
12 8.4 (emotion) 0 0
(social status) 12 8.4
People 1 0.7 (people) 1 0.7
(PEOPLE) 0 0
Content/Story 8 5.6
(activity) 7 4.9
(category) 1 0.7
(event) 0 0
109
Table 12 - continued
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Content/Story (cont.)
(setting) 0 0
(time aspect) 0 0
External Relation 6 4.2 (reference) 6 4.2
(similarity) 0 0
Art Historical Information
4 2.8
(ART HISTORICAL INFORMATION) 1 0.7
(artist) 1 0.7
(format) 2 1.4
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
2 1.4 (WAYWARD TERM FAILURE) 2 1.4
Description 3 2.1 (description) 3 2.1
TOTAL 143 100.1 TOTAL 143 100.1
Note: N = 39. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
This is the only cartoon which elicited a reference to the symbolic nature of editorial
cartoons, and that reference noted only that the entire image was “symbolic”. This cartoon is one
of only two that elicited a DESCRIPTION (as defined by Jörgensen’s 12 Classes) of any kind,
commenting on the nature of the man in the image. One of the WAYWARD TERM FAILUREs was a
confused attempt to comment on class structure in America, and the other was a reference to the
National Basketball Association.
4.1.3.9 – image “luck2” For this cartoon, most of the personal reactions centered on
displeasure with the situation being examined, that the participants disapproved of one or the
other sides in the 2011 NBA lockout, while the other such reactions instead focused on the
disgust over “reality TV”. In a unique turn in the tagging activity, the comments centering on the
theme of the cartoon focused not on what was happening in the real world, but rather on what
was happening in the image itself. The sole WAYWARD TERM FAILURE consisted of the word
“versus”.
110
Figure 9 luck2 [in color] (Luckovich, 2011c)
Table 13
Classes and attributes for”luck2” – tagging environment
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts 61 44.9
(abstract) 3 2.2
(atmosphere) 8 5.9
(theme) 50 36.8
(symbolic aspect) 0 0
Literal Objects 14 10.3 (object) 1 0.7
(text) 13 9.6
Viewer Reaction 25 18.4
(conjecture) 1 0.7
(personal reaction) 24 17.6
(uncertainty) 0 0
People-Related Attributes
14 10.3 (emotion) 0 0
(social status) 14 10.3
People 12 8.8 (people) 6 4.4
(PEOPLE) 6 4.4
Content/Story 6 4.4
(activity) 1 0.7
(category) 0 0
(event) 3 2.2
(setting) 1 0.7
(time aspect) 1 0.7
External Relation 1 0.7 (reference) 1 0.7
(similarity) 0 0
111
Table 13 - continued
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Art Historical Information
2 1.5
(ART HISTORICAL INFORMATION) 1 0.7
(artist) 1 0.7
(format) 0 0
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 0.7 (WAYWARD TERM FAILURE) 1 0.7
Description 0 0 (description) 0 0
TOTAL 136 100 TOTAL 136 99.7
Note: N = 39. Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity,
and are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes
are included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
4.1.3.10 – image “rame2”
Figure 10 rame2 [in color] (Ramirez, 2011c)
112
Table 14
Classes and attributes for”rame2” – tagging environment
Classes frequency of Classes
% of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts 43 33.1
(abstract) 6 4.6
(atmosphere) 12 9.2
(theme) 25 19.2
(symbolic aspect) 0 0
Literal Objects 19 14.6 (object) 4 3.1
(text) 15 11.5
Viewer Reaction 19 14.6
(conjecture) 0 0
(personal reaction) 18 13.8
(uncertainty) 1 0.8
People-Related Attributes
16 12.3 (emotion) 0 0
(social status) 16 12.3
People 7 5.4 (people) 0 0
(PEOPLE) 7 5.4
Content/Story 2 1.5
(activity) 0 0
(category) 0 0
(event) 0 0
(setting) 2 1.5
(time aspect) 0 0
External Relation 14 10.8 (reference) 14 10.8
(similarity) 0 0
Art Historical Information
4 3.1
(ART HISTORICAL INFORMATION) 1 0.8
(artist) 1 0.8
(format) 1 0.8
(technique) 1 0.8
(time reference) 0 0
Wayward Term Failure
4 3.1 (WAYWARD TERM FAILURE) 4 3.1
Description 2 1.5 (description) 2 1.5
TOTAL 130 100 TOTAL 130 100
Note: Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity, and are
not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order.
This cartoon produced the most direct references to an institution not shown in the image,
that being segregation in the United States as outlined by the Jim Crow laws, an institution
whose influence was also found in the perceived themes of the image, generally racism and its
associated social practices. This cartoon holds that largest percentage of the Atmosphere attribute
in the Abstract Concept class, also centering on racism. Opposite to this, there were some
participants who clearly did not perceive the reference to segregation in the United States. While
they did not correctly identify the objects in the image nor the references being made, they
113
nonetheless were able to correctly interpret overarching themes centering on racism found by
others in the image.
4.1.3.11 Review of tags by outside reviewer The outside reviewer noted that there
were few potential inconsistencies found between the definitions of the 12 Classes and their
application to the tags by the researcher. Most of these centered on the interpretation of the intent
of the participants, on questions pertaining to what aspect of the cartoons in question were being
spoken to. The largest portion of the questions regarding the researcher’s coding centered on
whether tags with Attributes such as Atmosphere and Abstract from ABSTRACT CONCEPTS
should also rightfully be included as Personal Reactions under the Class VIEWER REACTIONS.
The researcher found that such inclusion may occasionally be warranted, but not in every case.
Also noted with some regularity by the reviewer was the potential to reduce the number of
Wayward Term Failures by counting tags such as “US” and USA” as References under the Class
EXTERNAL REFERENCES. The researcher determined that while this may be desirable –
classifying the data as being better than relegating it to its own Class – that doing so in these
cases would be a questionable practice.
4.1.4 Summary of results: Tagging phase
Table 15
Summary results – tagging phase by Class with percentage of overall total
Classes # in
Class % of total attributes
total in attributes
% of attributes
Abstract Concepts 587 38.3
(abstract) 84 5.5
(atmosphere) 92 56.0
(theme) 409 26.6
(symbolic aspect) 2 0.1
Literal Objects 311 20.3 (object) 66 4.3
(text) 245 15.9
Viewer Reaction 222 14.5
(conjecture) 4 0.3
(personal reaction) 209 13.6
(uncertainty) 9 0.6
People-Related Attributes
130 8.5 (emotion) 10 0.7
(social status) 120 7.8
People 107 7.0 (people) 71 4.6
(PEOPLE) 36 2.3
Content/Story 75 4.9
(activity) 14 0.9
(category) 6 0.4
(event) 23 1.5
(setting) 23 1.5
(time aspect) 9 0.6
114
Table 15 - continued
Classes # in
Class % of total attributes
total in attributes
% of attributes
External Relation 41 2.7 (reference) 40 2.6
(similarity) 1 0.1
Art Historical Information
28 1.8
(ART HISTORICAL INFORMATION) 10 0.7
(artist) 8 0.5
(format) 8 0.5
(technique) 1 0.1
(time reference) 1 0.1
Wayward Term Failure 27 1.8 (WAYWARD TERM FAILURE) 27 1.8
Description 5 0.3 (description) 5 0.3
TOTAL 1533 100.1 TOTAL 1533 99.9
Note: Jörgensen’s Classes COLOR, VISUAL ELEMENTS, and LOCATION were never used in either the tagging activity or the query activity, and are
not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order.
Almost 40% of all tags made comment on some abstract concept represented in the
images, and of those 587 tags, 409 – almost 70% – made direct comment on the themes that the
participants thought were present, with the other four possible attributes in this Class being used
far less often (when they were used at all). In LITERAL OBJECTS, the Class is even more
dominated by one particular attribute – of the 312 tags found there, 246 of them referred to the
text found in the image, almost 79% of the total, with the remainder of the tags noting objects
within the image, and no note at all about clothing or body parts, the other two attributes in the
Class. In VIEWER REACTIONS, 94% of all responses were summary interpretations of the
cartoon’s meaning or intent, with the other two attributes in the Class – conjecture and
uncertainty – getting very little use. In PEOPLE-RELATED ATTRIBUTES, 92% of all tags centered
on the social status of people either depicted or thought to be alluded to overall. 64% of all the
tags fall into four of the 47 attributes available in the 12 Classes, and 84% of all the tags fall into
the four Classes that include those tags. The remaining Classes and attributes, when used, saw a
more evenly-distributed frequency of attributes.
Figure 11 shows that, for most of the Classes used, the range of frequencies of use is
small, and diminishes as the mean within each Class diminishes. As we might expect, the mean
for ABSTRACT CONCEPTS is highest, with even the lowest number of uses in a cartoon higher than
the highest in all but one other Class, demonstrating the pervasiveness of this class among all
cartoons. The use of LITERAL OBJECTS has the greatest range of use, from a high of 50 to a low
115
of 14. The range of PEOPLE is most affected by outliers; where the other two notable ranges have
a mean close to the middle of them, People has two very high frequencies – 33 and 22 – that
drastically affect the outcome in that without these cartoons, the mean would drop from 10.7. to
6.5.
0
10
20
30
40
50
60
70
80
Abstra
ct
Concepts
Lite
ral O
bje
cts
Vie
wer
Reactio
ns
People
-Rela
ted
Attrib
ute
s
People
Conte
nt/S
tory
Exte
rnal
Rela
tions
Art H
isto
rical
Info
rmatio
n
Wayw
ard
Term
Failu
re
Descrip
tion
High
Low
Mean
Figure 11 High-mean-low ranges for tagging activity
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
Abstra
ct
Conepts
Lite
ral O
bje
ct
Vie
wer
Resposes
People
-
Rela
ted
Attrib
ute
s
People
Conte
nt/S
tory
Exte
rnal
Refe
rence
Art H
isto
rical
Info
rmatio
n
Wayw
ard
Term
Failu
re
Descrip
tion
Female
Male
Figure 12 Comparison of tagging behavior by gender, by percent of overall totals
116
There is little difference between how the men and women who participated in this
research tagged the cartoons (see Figure 12). The 29 female participants tagged the cartoons in
such a way that 824 attributes were applied to the tags, averaging 2.85 attributes used per
cartoon, while the 22 males produced tags that garnered 715 total attributes for an average of
3.25 attributes used per cartoon. Figure 12 above shows that women are somewhat more likely
than men to use ABSTRACT CONCEPTS and PEOPLE-RELATED ATTRIBUTES to describe editorial
cartoons, while men are more likely to describe LITERAL OBJECTS and ART HISTORICAL
INFORMATION to do so, though it should be noted that most of the data for ART HISTORICAL
INFORMATION came from one participant and may be an outlier in the dataset. The other Classes
of image descriptor used by the participants appear to be evenly matched.
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
Abstra
ct
Conepts
Lite
ral O
bje
ct
Vie
wer
Resposes
People
-
Rela
ted
Attrib
ute
sd
People
Conte
nt/S
tory
Exte
rnal
Refe
rence
Art H
isto
rical
Info
rmatio
n
Wayw
ard
Term
Failu
re
Descrip
tion
Conservative
Moderate
Liberal
Figure 13 Comparison of tagging behavior by political leaning, by percent of overall totals
More differences can be seen in the tagging behavior in this study when the participants
are evaluated by political leaning. Conservatives produced 2.75 Classes per cartoon, moderates
produced 2.57 Classes, and Liberals produced 3.84 per cartoon, generally a full Class more than
either of the other two. Figure 13 above shows that there are some notable differences between
the frequency of use for the ABSTRACT CONCEPTS, LITERAL OBJECT, and VIEWER RESPONSE
Classes, with the three variables here each leading and lagging between the three outlooks for all
of these classes. Broken down to the attribute level, all three of the main attributes for ABSTRACT
CONCEPTS – Abstract, Atmosphere, and Theme – were most frequently used by moderates,
117
followed by liberals and then conservatives in each case. For LITERAL OBJECTS, conservatives
were most likely to note text in a cartoon, but liberals were most likely to note objects. Within
the VIEWER RESPONSE Class, the results are almost entirely populated with Personal Response
attributes, producing no difference at all between the participant groups on a per attribute level.
Figure 14 Comparison of tagging behavior by education, by percent of overall totals
Figure 14 above shows the largest differences seen between populations in this phase of
the research. It shows that degree holders are more likely to note LITERAL OBJECTS within an
image (mostly under the attribute Text), and far less likely to use ABSTRACT CONCEPTS (such as
Theme) and VIEWER REACTIONS (almost entirely Personal Responses) when describing editorial
cartoons. Additionally, the 19 degree holders used an average of 3.77 attributes per cartoon when
tagging, compared to the 32 non-degree holders using an average of 2.58 attributes per image.
Degree holders are about as likely to use ABSTRACT CONCEPTS to describe an editorial cartoon as
they are to describe a LITERAL OBJECT, where non-degree holders most often use ABSTRACT
CONCEPTS to describe such images, in numbers approaching half of all their tags.
It should be noted that the non-degree holding population had a mean age of 21.75 years,
while the degree holding population had a mean age of 34.74. When the same sort of description
is compared to this data based on age rather than education, with the binary nature of the
description preserved by dividing participants into two even groups (28 and under/29 and over
putting one more person in the former than the latter), the descriptions are seen to be largely the
same. The choice to attribute the differences seen here to education rather than age reflects the
118
intent of the initial recruitment effort and the subsequent serendipitous participation of non-
recruited audiences. While the initial recruitment was meant to elicit a resonant group that would
provide a rich and focused set of tags for the cartoons, the second group, while somewhat
different in focus, provided as many tags per cartoon as the first group, making it as rich.
4.2 Query phase
4.2.2 Participants
25 participants provided data for the query phase of this study. Of those, five self-
identified as conservative in their political leanings, 14 as Moderate, and six as Liberal. Eight of
the participants hold some sort of degree (Bachelor’s or above) and 17 do not. 16 participants are
female, and nine are male. All 25 participants tagged the ten cartoons between November 28 and
December 4, 2011. These cartoons can be found in Appendix G.
4.2.2 Query results
In the tagging phase, participants left a total of 1026 Classes for the ten cartoons.
Table 16
Summary data for the query phase
image name # of participants # of query parses avg. # of query parses/participant
ande1 25 95 3.80
bree1 25 101 4.04
hand1 25 95 3.80
luck1 25 98 3.92
rami1 25 109 4.36
ande2 25 108 4.32
bree2 25 116 4.64
hand2 25 84 3.36
luck2 25 115 4.60
rami2 25 105 4.20
Note: The term “query parses” refers to the number of discrete, tag-like parts of a full query.
Naming conventions from the first phase of the research were kept for the second phase
for clarity and consistency; the cartoons from Week 1 and Week 2 of the first phase were
presented together for the second phase. All of the participants for this phase of the research
participated in the first phase. This phase of the work yielded 4.10 attributes per participant on
average, compared to 3.92 attributes per participant in the first phase.
119
4.2.3 Results – Query Phase
4.2.3.1 – image “ande1”
Figure 15 ande1 [in color] (Anderson, 2011b)
Table 17
Classes and attributes for”ande1” – query environment
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts
30 31.6
(abstract) 2 2.1
(atmosphere) 6 6.3
(theme) 22 23.2
(symbolic aspect) 0 0
Literal Objects 19 20 (object) 4 4.2
(text) 15 15.8
Viewer Reaction 8 8.4
(conjecture) 0 0
(personal reaction) 8 8.4
(uncertainty) 0 0
People-Related Attributes
8 8.4 (emotion) 0 0
(social status) 8 8.4
People 9 9.5 (people) 0 0
(PEOPLE) 9 9.5
120
Table – 17 continued
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Content/Story 7 7.4
(activity) 0 0
(category) 3 3.2
(event) 0 0
(setting) 4 4.2
(time aspect) 0 0
External Relation
3 3.2 (reference) 3 3.2
(similarity) 0 0
Art Historical Information
7 7.4
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 7 7.4
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
2 2.1 (WAYWARD TERM FAILURE) 2 2.1
Description 2 2.1 (description) 2 2.1
TOTAL 95 100.1 TOTAL 95 100.1
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
There was a low number of overall attributes for this cartoon; 95 is the lowest recorded
total, found for this and one other image. Most affected by this seems to be the number of
ABSTRACT CONCEPTS found in the attributes for this image, about 32% of the total number
which, while the largest percentage found within this image’s description, is the lowest such
percentage found in the query activity. There is an unusually high number of DESCRIPTIONS
found here, pertaining entirely to descriptions of the plane that is central to the cartoon’s point.
4.2.3.2 – image “bree1” This image produced the highest number of LITERAL
OBJECTS described in the query phase of this research, noting the different animals within the
image as well as many references to the text on the signs the animals are holding; it is one of two
images that do not have ABSTRACT CONCEPTS as the most often occurring Class of description,
so much so that this cartoon produced the lowest number of notations of ABSTRACT CONCEPTS
(which may have been covered in the text for most cases) and of PEOPLE (which did not seem to
be central to the cartoon’s point).
121
Figure 16 bree1 [in color] (Breen, 2011b)
Table 18
Classes and attributes for”bree1” – query environment
Classes frequency of
Classes % of classes
attributes frequency of attributes
% of attributes
Abstract Concepts
27 26.7
(abstract) 2 2.0
(atmosphere) 0 0
(theme) 25 24.8
(symbolic aspect) 0 0
Literal Objects 33 32.7 (object) 10 9.9
(text) 23 22.8
Viewer Reaction 7 6.9
(conjecture) 0 0
(personal reaction) 7 6.9
(uncertainty) 0 0
People-Related Attributes
13 12.9 (emotion) 0 0
(social status) 13 12.9
People 2 2 (people) 1 1.0
(PEOPLE) 1 1.0
Content/Story 6 5.9
(activity) 0 0
(category) 5 5.0
(event) 1 1.0
(setting) 0 0
(time aspect) 0 0
122
Table 18 - continued
Classes frequency of
Classes % of classes
attributes frequency of attributes
% of attributes
External Relation
4 4 (reference) 4 4.0
(similarity) 0 0
Art Historical Information
6 5.9
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 6 5.9
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
3 3 (WAYWARD TERM FAILURE) 3 3.0
Description 0 0 (description) 0 0
TOTAL 101 100 TOTAL 101 100.2
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
4.2.3.3 – image “hand1”
Figure 17 hand1 [in color] (Handelsman, 2011b)
123
Table 19
Classes and attributes for”hand1” – query environment
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts
41 43.2
(abstract) 1 1.1
(atmosphere) 0 0
(theme) 40 42.1
(symbolic aspect) 0 0
Literal Objects 19 20 (object) 0 0
(text) 19 20
Viewer Reaction 8 8.4
(conjecture) 0 0
(personal reaction) 8 8.4
(uncertainty) 0 0
People-Related Attributes
3 3.2 (emotion) 0 0
(social status) 3 3.2
People 3 3.2 (people) 3 3.2
(PEOPLE) 0 0
Content/Story 6 6.3
(activity) 0 0
(category) 5 5.3
(event) 1 1.1
(setting) 0 0
(time aspect) 0 0
External Relation
0 0 (reference) 0 0
(similarity) 0 0
Art Historical Information
8 8.4
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 8 8.4
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
7 7.4 (WAYWARD TERM FAILURE) 7 7.4
Description 0 0 (description) 0 0
TOTAL 95 100.1 TOTAL 95 100.2
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
Most unusual for this cartoon is the high number of WAYWARD TERM FAILUREs, mostly
having to do with confusing the setting of the cartoon for an airport instead of a school; though
comparisons between the TSA and SAT testing procedures were not categorized here, outright
and exclusive declarations that this image dealt with airport security and related issues were
placed here. This high number may explain the overall low number of attributes found within the
image queries. There were also a very low number of PEOPLE noted in this image, unusual
because of the clear and certain depiction of two people within the image.
124
4.2.3.4 – image “luck1”
Figure 18 luck1 [in color] (Luckovich, 2011b) In the banner, the words “mission” and
“accomplished” are in yellow, while the other words are in white.
Table 20
Classes and attributes for”luck1” – query environment
Classes frequency of
Classes % of
Classes attributes frequency of attributes
% of attributes
Abstract Concepts
27 27.6
(abstract) 2 2.0
(atmosphere) 1 1.0
(theme) 24 24.5
(symbolic aspect) 0 0
Literal Objects 8 8.2 (object) 4 4.1
(text) 4 4.1
Viewer Reaction 12 12.2
(conjecture) 0 0
(personal reaction) 12 12.2
(uncertainty) 0 0
People-Related Attributes
0 0 (emotion) 0 0
(social status) 0 0
People 31 31.6 (people) 31 31.6
(PEOPLE) 0 0
Content/Story 4 4.1
(activity) 0 0
(category) 4 4.1
(event) 0 0
125
Table 20 - continued
Classes frequency of
Classes % of
Classes attributes frequency of attributes
% of attributes
Content/Story (cont.)
(setting) 0 0
(time aspect) 0 0
External Relation 7 7.1 (reference) 7 7.1
(similarity) 0 0
Art Historical Information
8 8.2
(ART HISTORICAL INFORMATION) 0 0
(artist) 1 1.0
(format) 7 7.1
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
0 0 (WAYWARD TERM FAILURE) 0 0
Description 1 1 (description) 1 1.0
TOTAL 98 100 TOTAL 98 99.8
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
This cartoon produced the most unusual descriptions of all the cartoons in the query phase
of this research. It is, comparatively, very low in ABSTRACT CONCEPTS and in LITERAL OBJECTS;
all other cartoons in this phase were found to be low in one or the other, but not both.
Additionally, there was no mention of PEOPLE-RELATED ATTRIBUTES at all, even though PEOPLE
are more often noted in this image than in any other. Also, this image produced the most uses of
EXTERNAL RELATION, centering mainly on the text in the banner and its playing off of a
previous, similar banner.
4.2.3.5 – image “rame1” This cartoon produced the largest number of VIEWER
REACTIONS, mostly participant interpretations of the message of the cartoon, but including a
noticeable number of expressions of outrage or questioning of the appropriateness of the trip. At
the same time, this cartoon produced the lowest incidence of use for the CONTENT/STORY class,
most of these noting that this image is, in fact, a cartoon. Contrary to what was found in the
cartoon bree1, this cartoon depicted no PEOPLE whatsoever, yet PEOPLE, such as President
Obama and Jay Leno, were used as search terms as much here as in most other cartoons.
126
Figure 19 rame1 [in color] (Ramirez, 2011b)
Table 21
Classes and attributes for”rame1” – query environment
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts
36 33.0
(abstract) 1 0.9
(atmosphere) 1 0.9
(theme) 34 31.2
(symbolic aspect) 0 0
Literal Objects 22 20.2 (object) 15 13.8
(text) 7 6.4
Viewer Reaction 17 15.6
(conjecture) 0 0
(personal reaction) 17 15.6
(uncertainty) 0 0
People-Related Attributes
9 8.3 (emotion) 0 0
(social status) 9 8.3
People 10 9.2 (people) 0 0
(PEOPLE) 10 9.2
Content/Story 5 4.6
(activity) 0 0
(category) 4 3.7
(event) 0 0
(setting) 0 0
(time aspect) 1 0.9
External Relation
4 3.7 (reference) 4 3.7
(similarity) 0 0
127
Table 21 - continued
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Art Historical Information
5 4.6
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 5 4.6
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 0.9 (WAYWARD TERM FAILURE) 1 0.9
Description 0 0 (description) 0 0
TOTAL 109 100.1 TOTAL 109 100
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
4.2.3.6 – image “ande2”
Figure 20 ande2 [in color] (Anderson, 2011c)
128
Table 22
Classes and attributes for”ande2” – query environment
Classes frequency of
Classes % of
Classes attributes frequency of attributes
% of attributes
Abstract Concepts
37 34.3
(abstract) 0 0
(atmosphere) 6 5.6
(theme) 31 28.7
(symbolic aspect) 0 0
Literal Objects 24 22.2 (object) 13 12
(text) 11 10.2
Viewer Reaction 13 12.0
(conjecture) 0 0
(personal reaction) 13 12
(uncertainty) 0 0
People-Related Attributes
8 7.4 (emotion) 0 0
(social status) 8 7.4
People 8 7.4 (people) 0 0
(PEOPLE) 8 7.4
Content/Story 11 10.2
(activity) 0 0
(category) 4 3.7
(event) 0 0
(setting) 0 0
(time aspect) 7 6.5
External Relation 0 0 (reference) 0 0
(similarity) 0 0
Art Historical Information
6 5.6
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 6 5.6
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 0.9 (WAYWARD TERM FAILURE) 1 0.9
Description 0 0 (description) 0 0
TOTAL 108 100 TOTAL 108 100
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order.
Generally speaking, this cartoon is closest to the overall average for frequency of use
across all nine relevant Classes of image description, which is to say that the results for this
cartoon most closely mirror the overall results for the query phase. Yet there remain some
oddities within the queries for this image. No depiction of a person is to be found within the
image, yet PEOPLE and PEOPLE-RELATED ATTRIBUTES comprise almost 15% of the total
descriptors for this cartoon. Symbols are used in place of “people,” and while the personages are
noted, the symbols are instead regarded as LITERAL OBJECTS. This cartoon uses the Thanksgiving
129
season as a way to frame the description of both the CONTENT/STORY and LITERAL OBJECTS, yet
was not found to use it as part of an EXTERNAL RELATION.
4.2.3.7 – image “bree2”
Figure 21 bree2 [in black & white] (Breen, 2011c)
Table 23
Classes and attributes for”bree2” – query environment
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts
32 27.6
(abstract) 3 2.6
(atmosphere) 0 0
(theme) 29 25.0
(symbolic aspect) 0 0
Literal Objects 29 25.0 (object) 15 12.9
(text) 14 12.1
Viewer Reaction 8 6.9
(conjecture) 0 0
(personal reaction) 8 6.9
(uncertainty) 0 0
People-Related Attributes
6 5.2 (emotion) 0 0
(social status) 6 5.2
People 18 15.5 (people) 18 15.5
(PEOPLE) 0 0
Content/Story 6 5.2
(activity) 0 0
(category) 5 4.3
(event) 1 0.9
130
Table 23 - continued
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Content/Story (cont.)
(setting) 0 0
(time aspect) 0 0
External Relation
8 6.9 (reference) 8 6.9
(similarity) 0 0
Art Historical Information
7 6.0
(ART HISTORICAL INFORMATION) 1 0.9
(artist) 0 0
(format) 6 5.2
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 0.9 (WAYWARD TERM FAILURE) 1 0.9
Description 1 0.9 (description) 1 0.9
TOTAL 116 100.1 TOTAL 116 100.2
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
This cartoon produced the highest number of descriptions in the query phase of this
research, and did so with very high numbers in categories not often used for other cartoons.
Found here were very high numbers for LITERAL OBJECTS, again mostly in the form of Text,
which in this case also identified the central person depicted in the image. This in turn led to a
very high number of instances of the Class PEOPLE, although this was not exclusively centered
on Bob Filner. This cartoon also produced the largest number of EXTERNAL RELATIONS, referring
exclusively to the Occupy Wall Street protests and other related events.
4.2.3.8 – image “hand2” This cartoon produced only 84 attributes in the query phase
of the research, by far the lowest total among the ten cartoons used. The only Class here that was
higher than average was ABSTRACT CONCEPTS, and it was quite a bit higher (25% of the total
attributes for this cartoon, as opposed to 18% of the total on average). Though a person is clearly
depicted in this cartoon, he is rarely mentioned in the tags, and neither is he described in detail in
PEOPLE-RELATED ATTRIBUTES.
131
Figure 22 hand2 [in color] (Handelsman, 2011c)
Table 24
Classes and attributes for”hand2” – query environment
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Abstract Concepts
39 46.4
(abstract) 6 7.1
(atmosphere) 0 0
(theme) 33 39.3
(symbolic aspect) 0 0
Literal Objects 17 20.2 (object) 3 3.6
(text) 14 16.7
Viewer Reaction 11 13.1
(conjecture) 0 0
(personal reaction) 11 13.1
(uncertainty) 0 0
People-Related Attributes
2 2.4 (emotion) 0 0
(social status) 2 2.4
People 2 2.4 (people) 2 2.4
(PEOPLE) 0 0
Content/Story 7 8.3
(activity) 3 3.6
(category) 3 3.6
(event) 0 0
(setting) 0 0
(time aspect) 1 1.2
External Relation
0 0 (reference) 0 0
(similarity) 0 0
132
Table 24 - continued
Classes frequency of
Classes % of Classes
attributes frequency of attributes
% of attributes
Art Historical Information
6 7.1
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 6 7.1
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
0 0 (WAYWARD TERM FAILURE) 0 0
Description 0 0 (description) 0 0
TOTAL 84 99.9 TOTAL 84 100.1
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
4.2.3.9 – image “luck2”
Figure 23 luck2 [in color] (Luckovich, 2011c)
133
Table 25
Classes and attributes for”luck2” – query environment
Classes frequency of
Classes % of
Classes attributes frequency of attributes
% of attributes
Abstract Concepts
42 36.5
(abstract) 2 1.7
(atmosphere) 0 0
(theme) 40 34.8
(symbolic aspect) 0 0
Literal Objects 4 3.5 (object) 0 0
(text) 4 3.5
Viewer Reaction 11 9.6
(conjecture) 0 0
(personal reaction) 11 9.6
(uncertainty) 0 0
People-Related Attributes
14 12.2 (emotion) 0 0
(social status) 14 12.2
People 8 7.0 (people) 0 0
(PEOPLE) 8 7.0
Content/Story 24 20.9
(activity) 0 0
(category) 3 2.6
(event) 19 16.5
(setting) 0 0
(time aspect) 2 1.7
External Relation 4 3.5 (reference) 4 3.5
(similarity) 0 0
Art Historical Information
8 7.0
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 8 7.0
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
0 0 (WAYWARD TERM FAILURE) 0 0
Description 0 0 (description) 0 0
TOTAL 115 100.2 TOTAL 115 100
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
This cartoon also produced a large number of overall instances of Class use, 115 in all.
Oddly, it produced the lowest number of LITERAL OBJECTS, in both direct notations of Text and
of objects in general. Opposite this, it did produce a relatively high number of PEOPLE-RELATED
ATTRIBUTES, almost twice as many (14) as it did PEOPLE (8). And this image produced the
largest number of comments pertaining to CONTENT/STORY among the cartoons in the query
phase, mostly dealing with the 2011 NBA lockout event.
134
4.2.3.10 – image “rame2”
Figure 24 rame2 [in color] (Ramirez, 2011c)
Table 26
Classes and attributes for”rame2” – query environment
Classes frequency of
Classes % of
Classes attributes frequency of attributes
% of attributes
Abstract Concepts
46 43.8
(abstract) 1 1.0
(atmosphere) 1 1.0
(theme) 44 41.9
(symbolic aspect) 0 0
Literal Objects 13 12.4 (object) 9 8.6 (text) 4 3.8
Viewer Reaction 9 8.6
(conjecture) 0 0
(personal reaction) 9 8.6
(uncertainty) 0 0
People-Related Attributes
21 20.0 (emotion) 0 0
(social status) 21 20.0
People 0 0 (people) 0 0
(PEOPLE) 0 0
Content/Story 8 7.6
(activity) 0 0
(category) 5 4.8
(event) 0 0
(setting) 3 2.9
(time aspect) 0 0
135
Table 26 - continued
Classes frequency of
Classes % of
Classes attributes frequency of attributes
% of attributes
External Relation 0 0 (reference) 0 0
(similarity) 0 0
Art Historical Information
4 3.8
(ART HISTORICAL INFORMATION) 0 0
(artist) 0 0
(format) 4 3.8
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
1 1.0 (WAYWARD TERM FAILURE) 1 1.0
Description 3 2.9 (description) 3 2.9
TOTAL 105 100.1 TOTAL 105 100
Note: N = 25. Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and
are not included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are
included if they were used for any cartoon in either activity, and are listed in alphabetical order. The other than 100% in the totals is due to
rounding error.
This cartoon was queried for by the participants in unexpected ways, when compared to
the other images in this phase of the research. While producing a close to average number of
total Classes (105), it did so by clustering in some Classes while ignoring others far more often
than in other images. This cartoon produced the largest number of ABSTRACT CONCEPTS, almost
44% of its total. It produced the highest number of PEOPLE-RELATED ATTRIBUTES, but did not
speak of PEOPLE at all. And it produced a relatively large number of DESCRIPTIONS of various
types.
Something not seen in these numbers is the confusion that this cartoon produced while
still making its point. Many participants did not seem to possess the visual literacy to properly
place the image in historical context; many missed that it was a visual reference to Jim Crow
laws in the American South in the 1950’s. Yet, those who lacked this knowledge still seemed to
be able to interpret the overall intent of the cartoon correctly, finding that it was about
segregation in general while not seeing that it was about a specific period.
4.2.3.11 Review of queries by outside reviewer The outside reviewer noted
somewhat more discrepancies between the stated rules for the Classes and their implementation
in the query phase of this research than were noted in the tagging phase. Where the possible
problems in the tagging phase centered on the interpretation of participant intent, most of the
problems noted in the query phase instead stemmed from possible researcher error. For one of
136
the cartoons reviewed, the reviewer correctly noted that the researcher had failed to include Text
as a LITERAL OBJECT in seven instances. In the other cartoon, three main inconsistencies were
found. The reviewer found that in addition to being an Object under LITERAL OBJECT, “Air Force
One” was also, in the context of the cartoon, a Setting under the Class CONTENT/STORY. The
reviewer also found that any part of a query that included “US” or “USA” should be counted as a
Reference under EXTERNAL REFERENCE (and not solely as Text), and questioned the inclusion of
“Tonight Show” (also Classed as Text) as a Reference, again in EXTERNAL REFERENCE. Upon
review, the researcher found that the inclusion of “Air Force One” as a setting makes sense in the
context of the cartoon (though not necessarily in all cases), but that the other two incongruities
noted in this cartoon are questionable because of the way that the Classes and their Attributes are
described.
4.2.4 Summary of results: Query phase
Table 27
Summary results – query phase by Class with percentage of overall total
Classes # in
Class % of total attributes
total in attributes
% of attributes
Abstract Concepts 357 34.8
(abstract) 20 2.0
(atmosphere) 15 1.4
(theme) 322 31.4
(symbolic aspect) 0 0
Literal Objects 188 18.3 (object) 73 7.1
(text) 115 11.2
Viewer Reaction 104 10.1
(conjecture) 0 0
(personal reaction) 104 10.1
(uncertainty) 0 0
People-Related Attributes
84 8.2 (emotion) 0 0
(social status) 84 8.2
People 91 8.9 (people) 55 5.4
(PEOPLE) 36 3.5
Content/Story 84 8.2
(activity) 3 0.3
(category) 41 4.0
(event) 22 2.1
(setting) 7 0.7
(time aspect) 11 1.1
External Relation 30 2.9 (reference) 30 2.9
(similarity) 0 0
Art Historical Information
65 6.3
(ART HISTORICAL INFORMATION) 1 0.1
(artist) 1 0.1
(format) 63 6.1
137
Table 27 - continued
Classes # in
Class % of total attributes
total in attributes
% of attributes
Art Historical Information (cont.)
(technique) 0 0
(time reference) 0 0
Wayward Term Failure
16 1.6 (WAYWARD TERM FAILURE) 16 1.6
Description 7 0.7 (description) 7 0.7
TOTAL 1026 100 TOTAL 1026 100
Note: Jörgensen’s Classes Color, Visual Elements, and Location were never used in either the tagging activity or the query activity, and are not
included in the results for this research. Classes are in the order of greatest frequency of use for both activities combined. Attributes are included
if they were used for any cartoon in either activity, and are listed in alphabetical order.
Similar to the tagging activity, the query activity showed almost 35% of all tags
commented on some abstract concept represented in the images, with over 90% making direct
comment on the Themes that the participants thought were present. In LITERAL OBJECTS, the
Class was less dominated by the attribute Text than it was in the tagging phase; just over 60% of
the tags center on the text within the images, where it was closer to 80% when tagging. In
VIEWER REACTIONS, all responses were either summary interpretations of the cartoon’s meaning
or intent, or rhetorical comments about the subject matter. Most of the remaining Class
frequencies were similar between the tagging and the query activities, with the exception of
CONTENT/STORY and ART HISTORICAL INFORMATION, which are used far more often in the query
portion of the study. These were driven by the use of the attribute Category for CONTENT/STORY
(almost half of which were allusions to the cartoon being a “joke” or “spoof”), and by the
attribute Format for ART HISTORICAL INFORMATION (almost all of which were allusions to the
cartoons being a “cartoon”).
Compared to the same measures in the tagging activity, we can see that the ranges for
frequency of use are somewhat larger in proportion, more varied, and major differences come
from different Classes (see Figure 25). The range for LITERAL OBJECTS is 29, from 33 to 4, and
closely mirrors that found in the tagging activity, where the range was 36, even though the Class
had 60% more instances of use in the tagging activity. Similarly, PEOPLE also had a range of 29,
from 31 to 2, where the range was 32 for the tagging activity, which had a similar number of
instances of use. CONTENT/STORY also had a very wide range in frequencies of use, from 24 to 4.
Both PEOPLE and CONTENT/STORY had one or two outliers affecting the mean. For PEOPLE,
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values of 31 and 18 raised the mean from six to ten; for CONTENT/STORY, the high value of 24
raised the mean from 6.5 to 8.4.
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Figure 25 High-mean-low ranges for query activity
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Figure 26 Comparison of simulated query behavior by gender, by percent of overall totals
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While there may be major differences in how men and women search for cartoons, we
must remember that in the tagging activity N=51, where here in the query activity N=25; caution
should be used when comparing these sets of results. These results seem to indicate that women
are far more likely to use ABSTRACT CONCEPTS to search for editorial cartoons, while men are
more likely to use components of the CONTENT/STORY and PEOPLE Classes. Women used an
average of 3.70 attributes per user per cartoon, while men used 4.82, a full attribute more than
the women used. While noteworthy, for women, n=16 and n=9 for men, so the appropriateness
of this measure suffers from the small sample size. Also, where the tagging activity showed
some use of ART HISTORICAL INFORMATION in cartoon description that mostly came from one
participant, in the query activity its use was much more spread out; eight of the nine male
participants indicated that “cartoon” was an appropriate search term, while five of the 16 women
did so.
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Liberal
Figure 27 Comparison of simulated query behavior by political leaning, by percent of overall
totals
More differences can be seen in the tagging behavior in this study when the participants
are divided by political leaning. Conservatives (n=5) produced 3.98 attributes per cartoon,
Moderates (n=14) produced 3.86 attributes, and Liberals (n=6) produced 4.77 attributes per
cartoon, almost an attribute more than either of the other two. Figure 27 above shows that there
are some notable differences between the frequency of use for most of the Classes when at least
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one of the subgroups uses the Class more than 10% of the time. Broken down to the attribute
level, all three of the main attributes for ABSTRACT CONCEPTS – Abstract, Atmosphere, and
Theme – were used by each of the subgroups, but Theme, the most frequently used of all the
attributes in total, was used less frequently among liberals than was the attribute Text and was
almost overtaken by the attribute Personal Reaction. Liberals used LITERAL OBJECTS more than
moderates and conservatives combined, but only moderates used the attribute Text exclusively,
where Conservatives and Liberals used about two Text descriptions for every Object description.
Similarly, liberals note PEOPLE more often that the other two groups combined, with
conservatives noting people either depicted or referred to only six times in 10 cartoons. But
where liberals dominated these last two categories, they did not use PEOPLE-RELATED
ATTRIBUTES at all, which is to say that they did not, in the course of generating queries for
editorial cartoons, ever refer to a person’s political ideology as a part of their searches.
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Figure 28 Comparison of simulated query behavior by education, by percent of overall totals
Figure 28 above shows the smallest difference in frequency of attribute use among its
subgroups – degree holders and non-degree holders – and is the opposite of the tagging activity,
which showed the largest differences in this grouping. The differences between the degree
holders (n=8) and non-degree holders (n=17) are present, but are small and may suffer from the
small sample sizes for both subgroups. The largest difference is in the use of LITERAL OBJECTS,
used 4.62% more often among degree holders than among non-degree holders.
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4.3 Comparison of results
4.3.1 Comparisons within this Research
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Figure 29 Comparison of frequencies of Class use between the tagging and simulated query
activities.
Comparing the results of the tagging and the simulated query activities to one another
shows that there is little difference between the two; each mimics the other, not perfectly, but
closely. The largest difference occurs in the Class ART HISTORICAL INFORMATION with the
simulated query activity using this Class 6.3% of the time and the tagging activity using it 1.8%
of the time. This is largely a result of the participants in the query activity noting that the images
in question may be found by using the word “cartoon,” which falls under the attribute Format for
this Class, where no such notation took place in the tagging activity. VIEWER REACTION also
shows a relatively large difference between the activities (tagging = 14.5%; simulated query =
10.1%), with the tagging activity showing the attribute Personal Reaction (by far the dominant
attribute found within this Class, in both activities) almost half again as often as in the simulated
query activity, though no particular reason as to why can be found, nor can any specific kind or
type of such comment be discerned. CONTENT/STORY shows a smaller difference (query = 8.2%;
tagging = 4.8%), due largely to the participants in the query activity using words like “joke” and
“satire” (found under the attribute Category) when searching for the cartoons in a simulated
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query environment, comprising almost half of the data in this activity. ABSTRACT CONCEPTS
shows a similar difference to that found in CONTENT/STORY (tagging = 38.1%; query = 34.8%)
but with no apparent trend, word, or reason to point to for a cause. The differences found within
the other Classes between the two activities are even smaller than these.
4.3.2 Comparisons to the Literature
Table 28
Summary of frequencies for Jörgensen’s 12 Classes across four sets of images in a free-tagging
environment
Jörgensen (1996)
Jörgensen (1998)
Laine-Hernandez & Westman (2007)
Landbeck (2012)
The 12 Classes Illustrations Illustrations Newspaper images Editorial Cartoons
Literal Object 29.3 34.3 29.1 20.3 (4.3) Color 9.3 9.2 6.2 0 People 10.0 10.3 7.0 7.0 Location 8.9 8.3 10.2 0 Content/Story 9.2 7.4 17.4 4.8 Visual Elements 7.2 7.2 4.0 0 Description 8.0 6.0 12.0 0.3 People Qualities 3.9 5.2 8.7 8.5 Art Historical Info 5.7 3.8 0 1.8 Viewer Reaction 2.9 3.7 3.6 14.5 External Relation 3.7 3.3 0.3 3.0 Abstract Concepts 2.0 3.0 1.7 38.1
Note: the percentages for Landbeck exclude the emergent Class WAYWARD TERM FAILURE. Parenthetical data for LITERAL OBJECT under
Landbeck indicates the frequency percentage when Text is not included in the total.
Editorial cartoons produced 12 to 22 times the amount of ABSTRACT CONCEPTS in this
research than did other research conducted in a similar fashion. For editorial cartoons, this Class
is composed mostly of tags with the attribute Theme and tended to deal with the overarching
messages found within the cartoons in question. While it seems that the Class LITERAL OBJECT
may be quite similar across all four research efforts, the numbers for editorial cartoons are
composed almost entirely of the attribute Text, a feature found far more often in editorial
cartoons than in illustrations or in newspaper images. Without Text, this entry would read 4.3%.
Compared to the other research, the frequency of VIEWER REACTION found for editorial cartoons
is much higher, possibly because of the evocative nature of such images. Where Jörgensen and
Laine-Hernandez & Westman found occasional uses of the Classes DESCRIPTION, VISUAL
ELEMENTS, LOCATION, and COLOR, this research almost never found that these were used to
describe editorial cartoons.
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Table 29
Summary of frequencies for Jörgensen’s 12 Classes across three sets of images in an image –query environment Jörgensen (1996) Jansen (2007) Chen (2000) Landbeck (2012)
The 12 Classes Illustrations Excite.com Assignment Editorial Cartoons
Literal Object 27.4 21.7 25.4 18.3 (4.9) Color 9.7 1.0 0.5 0 People 10.3 30.2 10.8 8.9 Location 10.7 4.0 32.6 0 Content/Story 10.8 0.3 1.0 8.2 Visual Elements 5.4 0.8 2.3 0 Description 9.0 30.5 1.1 0.7 People Qualities 3.9 3.4 8.4 8.2 Art Historical Info 5.7 0 12.8 6.3 Viewer Reaction 1.9 0.2 0 10.1 External Relation 3.8 0 0.8 3.0 Abstract Concepts 1.5 7.9 4.4 34.8
Note: The percentages for Jansen were re-calculated to exclude three additional Classes that resulted from the research: Cost, URL, and
Collection. The percentages for Chen were recalculated to show the total percentage of each class that was agreed upon by at least two out of
three coders. The percentages for Landbeck exclude the emergent Class WAYWARD TERM FAILURE. Parenthetical data for LITERAL OBJECT under
Landbeck indicates the frequency percentage when Text is not included in the total.
The salient points about the tagging activity’s comparison to other literature also appear
to be true for a similar comparison for the simulated query activity: ABSTRACT CONCEPTS are far
more dominant when searching for editorial cartoons, LITERAL OBJECT is again dominated by the
attribute Text, VIEWER REACTIONS play a larger role for these images than for others, and four
Classes that are at least somewhat useful in searching for other types of images are far less so
when searching for editorial cartoons. Both seem to indicate that there are few similarities
between Class frequency when either tagging or simulating a query for editorial cartoons and
those frequencies found in similar activities for other kinds of images. But there are other
indications that there may be some secondary considerations that would help shed light on these
efforts to describe editorial cartoons in specific, and images in general.
4.3.3 Post hoc observations
In graphing the tagging results shown in section 4.2, we find the following:
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Figure 30 Comparison of frequencies among tagging studies, with Classes in alphabetical order
per study
There is no apparent order to be found within this set of results; while some Classes
across the four studies are within 10 percentage points of one another, others differ wildly,
notably ABSTRACT CONCEPTS. LITERAL OBJECTS seems to be popular across all the noted
research and holds a comparatively small difference between the minimum and maximum
values, only nine percentage points. But the next most used Class, CONTENT/STORY, has more
than double the difference. The degree of variability diminishes with the overall frequency of
use, with the notable exception of ABSTRACT CONCEPTS, where editorial cartoons appear to be an
outlier. As seen here, there seems to be no overarching pattern to user behavior when tagging
images.
When we order the Classes within each study by rank – regardless of what Class may be
first in one and eighth in another – we begin to see a possible pattern emerge, namely that one
particular Class tends to be used 30-40% of the time, the next most often-used Class is used
about half as often, and the remaining Classes are used in steadily decreasing frequencies.
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1 2 3 4 5 6 7 8 9 10 11 12
Jörgensen (1996)
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(2007)
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Figure 31 Comparison of frequencies among tagging studies, with Classes in rank order per
study
The pattern shown here shows a relative degree of uniformity, not in how often specific
Classes of image description are used, but in how those frequencies tend to be distributed. Large
differences between the minimum and maximum values in any given rank are larger at the
beginning and much smaller and more uniform after the third value. Thus, we can see that while
a disparate collection of images – illustrations, data-based images, photos from news magazines,
and editorial cartoons – produce different frequencies of use for any particular Class, they tend to
use two Classes far more often than the other ten, with a gradual reduction in frequency of use
from the third most-used Class to the twelfth.
Similarly, there is no discernible pattern to be found when comparing query results using
Jörgensen’s 12 Classes, aside from, once again, LITERAL OBJECT being used most often and at
close to the same rate.
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Jörgensen (1996)
Jansen (2007)
Chen (2000)
Landbeck (2012)
Figure 32 Comparison of frequencies among simulated query studies, with Classes in
alphabetical order per study
We see that, as before, there are substantial variations in the frequency of use for each individual
Class. Chen seems to have an unusual emphasis on LOCATION, Jansen on DESCRIPTION and
PEOPLE, and Landbeck on ABSTRACT CONCEPTS. PEOPLE follows ABSTRACT CONCEPTS in
frequency of use, but shows more variability than is found with the latter. ABSTRACT CONCEPTS,
PEOPLE, and LOCATION are used with close to the same overall frequency, and show similar
differences between the minimum and maximum usage, after which lesser frequency breeds less
variability, just as before. This figure seems to show less uniformity in the use of individual
Classes in the query activity than what was found in the tagging activity.
This lesser uniformity is found again when we order the Classes by rank:
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1 2 3 4 5 6 7 8 9 10 11 12
Jörgensen (1996)
Chen (2000)
Jansen (2007)
Landbeck (2012)
Figure 33 Comparison of frequencies among simulated query studies, with Classes in rank order
per study
As before, greater discrepancies in the frequency of use for each rank are found at the beginning
than at the end, but this is more evident in the query activity than it is in the tagging activity. As
before, the first rank shows a moderate level of variability, while the second and third ranks
show more before finding a relatively uniform decline in both frequency of use and in in-rank
variability. While the same types of trends found in the tagging activity can be seen here, there is
less uniformity – a less tightly-bound set of trends – found here.
4.4 Interviews
The interviews were conducted with an eye toward answering three main questions: in
what order would professionals in the field predict the 12 Classes would be used in the
description of cartoons; what do these professionals think of the results of this study; and would
these results would make any difference in their professional work. These interviews were
conducted in an unstructured manner as determined on an interview-by-interview basis; the order
and structure of the interviews was dictated by the interviewee’s responses to the initial interview
questions. Some preferred to give the entirety of their list first, then to hear the results, then to
discuss the professional implications of those results, while others wished to address the
professional or philosophical repercussion of the results on a class-by-class basis.
The interviews were not meant to shed light on the nature of how cartoons are described,
as was the focus of the tagging and query phases. Instead, the focus of these interviews was to
see how and where the results from the first two phases fell into the perceptions of the image
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professional and the professional cartoonist. These interviews were conducted not despite the
user or reader or viewer, but on their behalf, because it is professionals such as these that
produce the images in question and surrogate them for retrieval from large information systems.
While the opinions of these interviewees did not change the results in any way, they do give
some guidance as to how welcome the results are and how they might best be implemented.
A total of seven professionals from either the cartooning profession or the image
preservation and access fields were interviewed, with questions centering on whether the results
from the previous two phases of this research were surprising to them, and whether any aspect of
their professional work might be altered by these results. Three of the interviewees were
professional cartoonists, two were self-professed cartoon historians, two dealt exclusively with
preservation and access, two spent time as reporters for newspapers, two were in academia, and
two worked for the federal government of the United States.
The Web-based service recordmycalls.com (2012) was used for recording the interviews.
After the first interview, it became obvious that there was a delay between one person speaking
and being heard by the other, which necessitated an adjustment on the part of the researcher
when engaging in the back-and-forth of the interview process. BizScription Inc. (2012) was used
for transcription services, as they were recommended by recordmycalls.com as charging less and
having quicker service. Only the pertinent portions of the interviews were transcribed; the re-
reading of the informed consent portion at the beginning of the interview, and the exchange of
source-specific information that was incidental to the research questions was left out. For
purposes of confidentiality, all interviewees are referred to in the feminine.
4.4.1 Interviewees
Interviewee #1: An image professional in a research library at a large public university in
the southeastern United States. She began working in that capacity two years ago, after earning
her Master’s degree in Library and Information Science from an ALA-accredited university.
Interviewee #2: A retired editorial cartoonist in a small market in the American northeast.
She earned her Master’s in English some years ago, was active in the governance of the
Association of American Editorial Cartoonists, and has accumulated a vast library of books,
anthologies, and collections pertaining to editorial cartooning in general, and is a self-proclaimed
cartoon historian.
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Interviewee #3: An academic in a teaching college in the southeastern United States. She
had an extensive background in journalism, having earned a Bachelor’s degree in the field and a
Master’s in a related field before spending over 20 years working as a correspondent for a major
American news network and as a reporter and award-winning editor for a newspaper covering a
major metropolitan area in the United States.
Interviewee #4: Had no direct interest in cartoons per se, but had an abiding professional
interest in the preservation and access to images in general for a large federal agency in the
United States. More specifically, she dealt with digitization standards across several United
States agencies and organizations, and was trying to write guidelines that would allow for
universally applied specifications for such efforts. She holds a Master’s in Fine Arts in
photography.
Interviewee #5: An active and award-winning editorial cartoonist for a medium-market
newspaper in the mid-Atlantic region of the United States. Holding a Bachelor’s degree in
Journalism, she started her career as a reporter and editor before becoming a full-time cartoonist.
She too claims the title of cartoon historian, having amassed a smaller but substantial collection
of historical works on the subject, as well as collections of work for certain other cartoonists.
Interviewee #6 holds a PhD in History and a Master’s in Library and Information Science
from an ALA-accredited school. She has twenty years’ experience for a large federal document
management organization, most of which has been spent dealing with images. She works with
images such as editorial cartoons, among others, on a regular basis.
Interviewee #7: An active, award-winning editorial cartoonist based in the eastern United
States. She was working with alternate media for her cartooning, and had left standard, static
cartooning behind. She earned a Bachelor’s in Fine Arts before working for a major movie
studio, after which she moved into cartooning.
4.4.2 Central interview questions
While a great deal of supplementary evidence was collected over the course of the
interviews, much of it was not germane to the research questions of this dissertation; while
interesting to the researcher, and potentially the inspiration for other research, this did not shed
much light on the main thrust of the research being conducted here. Three central questions were
asked of each of the interviewees.
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4.4.2.1 Pre-results predictions Each of the seven interviewees was asked which of
Jörgensen’s 12 Classes would most often be used. All seven of the interviewees thought that the
Class LITERAL OBJECT would be among those most often used. Most found that the major objects
within an image, those that had something to do with the overall setting or message of the
cartoon, would usually be noted by average, everyday cartoon readers. One interviewee stated
flatly that the text within a cartoon – which counts as a LITERAL OBJECT according to Jörgensen
– would not be noted, unless it happened to name an object or person within the image. None of
the seven people interviewed placed this as their most often used Class, nor did it seem to
generate a great deal of certainty or enthusiasm. It was generally chosen with an attitude of
inevitability, as if to say, “Of course, LITERAL OBJECTS will be among the most used; no need to
even ask”.
Six of the seven interviewees thought that both PEOPLE and CONTENT/STORY would be
among the most noted of the Classes, often with equal levels of fervor and certainty, and quite
often together. Both were seen as central to either the understanding of an editorial cartoon, the
point of creating such an image, or both. PEOPLE was taken as it was intended, to mean the actors
or participants within a cartoon, and as such were described with some confidence as the reason
that a cartoon could exist, or as the instigators of the event depicted in the cartoon. Interviewee
#1 took the definition of the Class to mean that it only included personal pronouns, and thus gave
it a small chance to be used. CONTENT/STORY was usually taken at face value to be synonymous
with Event, which is an attribute of the Class but not its sum total. With a confidence roughly
equal to that of PEOPLE, interviewees predicted that the occasion that spawned the editorial
cartoon would be among the usual descriptors of the image. Interviewee #6 went so far as to say,
“…I would say that with editorial cartoons, unless there are no people in it, people are first.”
ABSTRACT CONCEPTS was picked as a common descriptor by five out of seven of those
interviewed. Neither of the two who did not pick this category realized that it included the
attribute Theme; all five of those who did pick this Class realized that this was so, and cited it as
the main reason for choosing this class. Of this, Interviewee #2 stated,
The first thing you’re going to search for if you’re searching for [a cartoon] is the
subject, I mean the subject it deals with, which interestingly, unlike images, can
sometimes be something that isn’t even shown in the cartoon itself.
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In a similar vein, Interviewee #3 saw that since this is the sole Class that must be found among
all editorial cartoons, she assumed that it would be the most often found description of such
images. None of the other attributes of the Class were noted, except for Interviewee #7’s
speculation that Symbolic Aspect may occasionally be used when describing an editorial
cartoon.
Four of the seven interviewees thought that PEOPLE-RELATED ATTRIBUTES would be
among the most used Classes of descriptions for editorial cartoons. Said Interviewee #7:
I think that definitely is a very strong thing that people see because that's
frankly… something that we use in our visual language is how do we dress the
people, how do we draw the people in terms of their dress as well as their body
attitudes and we know what type of social status the person is. That's a big part of
an editorial cartoon when you're trying to convey a point of view.
Others focused on how this Class encompasses such things as the liberal/conservative and
Republican/Democrat dyads.
Other noted Classes were predicted to be often used by less than half of those
interviewed. COLOR was thought by two to be important in conveying mood or tone within a
cartoon. VISUAL ELEMENTS was noted by two cartoonists, who speculated that while it was
certainly an important part of the cartoon itself, most readers would probably not use it to
describe a cartoon per se. ART HISTORICAL INFORMATION was noted by one interviewee as the
only part of the 12 Classes where one could specify that one was searching for a cartoon.
DESCRIPTION was seen as an extension of visual language by one interview participant.
EXTERNAL RELATIONS was cited as likely to be used by one interviewee, who said so because of
the need to relate the content of an image to its context, which may not be shown within the
cartoon itself.
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Figure 34 Tag cloud of interviewee’s predictions
4.4.2.2 Post-results comparison When told that ABSTRACT CONCEPTS was by far
the most-often used of the 12 Classes among regular, everyday cartoon readers, the collective
answer was one of immediate belief, spanning from “well, of course it is” to “how could it be
otherwise?”. Of this, Interviewee #2 stated, “…you ask what’s [the cartoon] about, you mean
what subject, what is the topic the person is talking about, or in this case drawing about,
regardless of what specific pictures they use to draw something about that subject.” One of the
seven interviewees did not chose ABSTRACT CONCEPTS to be one of the most used Classes but,
when it was explained to her that the Class included the attribute Theme, she immediately saw
that she should have included it in her choices. That the topic, subject, aboutness, or theme of a
cartoon was the most often noted aspect among all the aspects was a surprise to no one.
LITERAL OBJECTS was the second most used of the Classes, something predicted by all
seven of the people interviewed, all of whom believed that the noting of the various objects,
major and minor, within an image would occur regularly among everyday readers. When it was
explained that the Class was indeed often used but that Text was noted about four times as often
as were actual objects in both contexts, they were surprised. In this, two suggestions were made
about future testing. First, some care should be taken to include a cartoon with no text within the
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set of test images, because wordless cartoons are often what cartoonists are striving for. Second,
that this focus on text may be pertinent to American cartoons more than for European cartoons,
because the norm for the French, the English, the Germans, and so on is to not use text at all,
where the opposite is true here.
That VIEWER REACTIONS was the third most used Class of image descriptor for cartoons
was as much a surprise to the interviewees as it was to the researcher, and little explanation for
this could be guessed by those interviewed. Most of the conjecture about why this Class was
used so many times centered on the notion that it was a byproduct of the data gathering process,
something about which Interviewee #7 said, “I don't think they'd realize it, I think that they just
would do it.” No one could see any utility to including such information as part of a record for
editorial cartoons. Said Interviewee #3: “Viewer response is not an image description. It’s
important, but it’s not an image description.” Two interviewees found that, even though some
sort of reaction was sought to the cartoons by the artists, the inclusion of such information within
a record might be a problem, as the reaction from one side of an issue might produce a record
that seemed to represent the issue in a skewed manner, although the inclusion of summary data
about a large number of reader responses might be of some use.
PEOPLE and PEOPLE-RELATED ATTRIBUTES were reported as being neck-and-neck in
terms of frequency of use. While this did not surprise anyone, neither did it seem the natural
course. Interviewees seemed to fall on one side of the dyad or the other, favoring either PEOPLE
or PEOPLE-RELATED ATTRIBUTES, but usually not both. While most of those interviewed (six of
seven) could see the utility of noting which people were pictured, the importance of PEOPLE-
RELATED ATTRIBUTES was seen as one of the primary means of communicating the artist’s point
to readers. Interviewee #7 went so far as to say:
I think that definitely is a very strong thing that people see because that’s
frankly… that’s something that we use in our visual language is how do we dress
the people, how do we draw the people in terms of their dress as well as their
body attitudes and we know what type of social status the person is. That’s a big
part of an editorial cartoon when you're trying to convey a point of view.
4.4.2.3 Effects of data on practice Very little effect on practice resulting from these
findings is predicted by any of the interviewees. None of the professional cartoonists said that
they would change anything about the way they compose their images as a result of this research.
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While they found the results interesting, and generally expressed surprise at the lack of LITERAL
OBJECTS noted by participants, they each felt that they had already mastered their art to the
degree that they were able, and that no changes were warranted. The various image professionals
echoed these sentiments, save for one. In general, those who dealt with cataloging and preserving
images focused their work on things other than descriptive metadata. The one professional whose
work did deal directly with such information found that the emphasis shown to the subject or
subjects of the cartoon, coupled with the lack of actual objects when describing such images,
may change cataloging practices for editorial cartoons within her organization, and the training
of volunteer indexers may change in some small ways as well, but that institutional momentum
would be difficult to overcome.
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CHAPTER 5
DISCUSSION, IMPLICATIONS, & CONCLUSIONS
Jörgensen’s 12 Classes can be reasonably used to describe editorial cartoons, though
there are some Classes that, when used for this kind of image, may benefit from revision in
definition or from dividing an often-found Class with a dominant attribute into two, separate
Classes. The Classes were not conceived with the description of images in general in mind, but
were the result of an effort to classify the tags given to sample images from a catalog of
illustrations; the Classes are not meant to describe anything but what was seen in Jörgensen’s
(1995) original research. That a portion of the academic community has taken these Classes and
used them to help classify descriptive efforts for other kinds of images is both a testament to the
utility of the Classes and a basis for descriptive efforts among disparate kinds of images. That the
descriptions of editorial cartoons resulting from this research largely found reasonable
classifications within the 12 Classes is not surprising, but the findings from the other, similar
research cited showed that the frequency of use for the individual Classes within this image type
is.
The results of this research show that while editorial cartoons can be described using
Jörgensen’s 12 Classes, they are described in very different ways than are other images. When
comparing the results of this research to that of previous, similar work, the frequency of
ABSTRACT CONCEPTS seems to be a surprise. Based on the results of the preceding works, we
might expect a very low percentage of tags that could reasonably be described as ABSTRACT
CONCEPTS, perhaps with some variation (as seems common when comparing different image
types), but not the very large percentage found in this research. Similarly, the results show that
there are comparable differences in the frequency of Personal Reaction in terms of comparative
abundance, and in DESCRIPTION, COLOR (even with eight of the ten images being in full color),
LOCATION, and VISUAL ELEMENTS when considering comparative scarcity. The former may be
explained when the nature of the images themselves are considered, and the latter in the context
of the former.
The results here seem to indicate that editorial cartoons are very different images than are
illustrations, scientific diagrams, and images from news magazines. Editorial cartoons can be
said to be created for the purpose of conveying meaning, for getting across the feelings and
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insights of the cartoonist regarding a particular political or social issue, whereas illustrations are
meant to provide a visual break in text and to provide subtext for the textual content of the book.
Likewise, diagrams are meant to convey raw data for user consumption, and the images in a
news magazine would largely be included there to record what people, events, and settings
looked like, serving to record the visual elements of larger events. This inspiration – the thing
which breathes life into the image and gives it purpose – is very different among these types of
images, and as such they carry with them different meanings in the eyes of those who describe
them.
The reason why this may be so might center on the point of the respective types of image,
on why they were created, and on what the images depend for proper interpretation. Editorial
cartoons seem to be created for the purpose of conveying to readers ideas that might accurately
be described as abstract concepts, and for the purpose of inspiring viewer responses. It would
follow that since they were created for these purposes, that they would then be described mostly
on a similar basis. Similarly, images such as the illustrations used in Jörgensen’s research were
created with an eye toward decoration or appeal and providing a desired environment of
supplementing textual information, thus causing viewers to comment more on those aspects of
the images.
5.1 Discussion
5.1.1 Theory
To relate the findings in this research to the cited theories that provided a lens though
which the work proceeded, a certain amount of speculation is in order. Panofsky’s (1939)
theories concerning iconology were meant to aid in the description of Renaissance art, but have
some utility in describing images in general. As previously discussed, his three levels of
description call for increasing familiarity with both the subject matter pictured in the image and
with the context in which the image was meant to be viewed before an accurate and complete
verbal description could be properly constructed. Shatford-Layne (1994) made these ideas more
useful for images in general by adding the ideas of specific and generic descriptions within
Panofsky’s levels, allowing some descriptive actions to fall more readily into one of the three
levels by examining the specificity of the descriptions. Fidel (1997) found that images have
different requirements for full description based on whether they are meant to simply record
what an object looked like or if it were instead meant to be a pointer to a bigger event.
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Jörgensen’s 12 Classes are central to the conduct of the research described in this work. To
compare theory to practice, it seems necessary to describe the latter in terms of the former.
Were we to merge the ideas from Panofsky’s theory of iconology and the practical
applications of Shatford-Layne’s split of the Generic and the Specific, then were to apply these
to Jörgensen’s 12 Classes, we might divide those Classes like this:
Table 30
Division of Jörgensen’s 12 Classes by the theories of Panofsky, Shatford-Layne, and Fidel Object Data
Pre-iconographic Iconographic Iconologic
Literal Object (generic) Literal Object (specific) Abstract Concepts People (generic) People (specific) Content/Story People-Related Attributes (generic)
People-Related Attributes (specific)
External Relations
Color Description Visual Elements Art Historical Information Location
Note: this table does not include the Classes VIEWER REACTION or WAYWARD TERM FAILURE
In this, named items – those that use a proper noun, for example – in an image would be
Classed as specific LITERAL OBJECTS, while non-specific objects would be Classed as generic
LITERAL OBJECTS. Similar arrangements would apply to PEOPLE and to PEOPLE-RELATED
ATTRIBUTES. Items with the attribute Text would fall under specific LITERAL OBJECTS, and the
identification of a particular political party would fall under specific PEOPLE-RELATED
ATTRIBUTES. Overall, it shows the progression from less foreknowledge to greater
foreknowledge in correctly interpreting the constituent parts of an image and of the image as a
whole. While it may be true that the utility of this breakdown is limited by the lack of definitions
for each class (for instance, how does one determine the generic from the specific?), it is useful
for a general discussion of how Jörgensen’s Classes can be viewed vis-à-vis Panofsky, Shatford-
Layne, and Fidel.
If we were to accept this breakdown of the Classes as per the theories, we would then
find that the following is true for the research presented here:
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Table 31
Division of Jörgensen’s 12 Classes by the theories of Panofsky, Shatford-Layne, and Fidel, with
results from both activities Object Data
Pre-iconographic Iconographic Iconologic
Literal Object (generic) 94 Literal Object (specific) 406 Abstract Concepts 944 People (generic) 3 People (specific) 118 Content/Story 158 People-Related Attributes (generic)
80 People-Related Attributes (specific)
205 External Relations 76
Color 0 Description 12 Visual Elements 0 Art Historical
Information 93
Location 0
177 834 1178
Note: this table does not include the Classes VIEWER REACTION or WAYWARD TERM FAILURE
We see that most tags would fall into Panofsky’s iconological level of description, also
known as iconography in the deeper sense. It would seem to indicate that most participants either
viewed the editorial cartoons as a comment on larger issues (as per Fidel) or as an assembly of
comments on several esoteric issues (as per Panofsky and Shatford-Layne). In either case, it is
clear that a majority of tags for editorial cartoons involve a deep understanding of the issues
being spoken to, and that the generic details are of little import when describing such images.
Mai’s (2005) ideas concerning domain analysis seem to mirror those put forth in the
theories of Panofsky, Shatford-Layne, and Fidel. Where these three ideas revolve around the
concept that a depth of knowledge allows for a depth of description, Mai shows that such
circumstances are necessary for the full and proper surrogation of images specifically, and
documents in general. In asserting that the reader’s reaction is, in fact, the meaning of a text (or,
in this case, image), he shows that a sufficient understanding of the domain that an editorial
cartoon falls into – as defined by the event, the actors in that event, and other such stage-setting
information – is essential to the correct and full interpretation and description of that image,
mirroring the ideas of Panofsky’s levels of meaning and Shatford-Layne’s dyad of the specific
and the generic. Further, he presents the notion that the meaning of a text cannot be divorced
from the use of that text, lending further credence to the idea that to understand an editorial
cartoon, one must first understand the issues and events that inspired it in the first place.
Interestingly, Mai points out that language is dynamic, and that the words used to describe a
document today might not be as accurate in the future (such as is seen in the disconnect between
Bush’s “memex” and the present-day Internet). This leads to considerations surrounding the use
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of specific vocabularies to describe editorial cartoons in the present day that would be of
diminished value to future generations. Such ideas are echoed by Hjørland (2001) when he
points out that relevance measures within the surrogate itself are of limited value because such
things are at least partially defined by consensus, leading to the aging of descriptive terms along
generational, technical, or social lines.
5.1.2 Previous studies of cartoon interpretation
DeSousa and Medhurst (1968) found that college students could not reliably pick
appropriate words from a list to correctly describe editorial cartoons, Bedient and Moore (1982)
found that perhaps a third of responses from public school students could reasonably be called
“correct,” and Carl (1968) found that more than two-thirds of responses from adults were in
conflict with the intent of the cartoon’s author. Yet in this work, tags falling into the Class
WAYWARD TERM FAILURE occur at a far lower rate than those found in previous studies. This
research made no effort to ascertain the correctness of participant’s work, nor did it attempt to
ask the artists in question as to their intended messages, so a perfect comparison of the results in
this research to that in other works is not possible. But there is no evidence that the participants
in this research produced what might reasonably be called “wrong answers” on anything
approaching the scale found in previous research: in this study, the largest percentage of what
might be termed “wrong answers” was found to be less than 8% for the tagging activity and less
than 5% in the query activity, both of which are orders of magnitude less than those found in the
previously mentioned studies, possibly indicating that people are now better at correctly
interpreting the subjects being discussed in editorial cartoons, or cartoons have gotten better at
communicating their intended messages, although it should be noted that all of the participants
were engaged in some level of academic activity, a potentaill ylimitng factor for this particular
Class.
But it is difficult to imagine exactly what would qualify a set of tags as correct, or, more
importantly, correct enough. None of the cited research describes in detail criteria for a correct
answer or response, nor do they outline any sort of scale or system for determining the
correctness of an answer. For instance, if a cartoon featured a caricature of Hillary Clinton,
would it be enough to say that part of the image depicted Hillary Clinton, or would a “correct”
answer be “Hillary Rodham Clinton”? Or “Secretary of State Hillary Rodham Clinton”? If a
cartoon held images of sporting triumph for a given country in, say, a World Cup soccer match,
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is it correct enough to state that one team won, or would a complete answer also state that
another team lost? Would questionable officiating also need to be noted? And how many would
need to question it before it became notable? In cases where two countries are diametrically
opposed in some sort of conflict, how would a cartoon collecting entity determine the proper
terminology for the description of themes and actors in a cartoon; are they terrorists, or freedom
fighters?
It might be argued that an analysis of the intended audience would be in order, which
would help to determine the lens through which a cartoon ought to be viewed and, consequently,
the best set of terms for describing such things. But this plays havoc with the creation of
surrogates for cartoons in large collections. Using terms that the intended audience would use
limits the utility of the description to the users of that collection and to other, similar groups of
users; users who are separated by politics, geography, time, or religion would find little utility in
records aimed specifically at another audience. It would then follow that a policy of neutral
wording should be followed whenever possible; an event would be neither a “terrorist bombing”
nor a “blow against the oppressor,” but a note that a bomb exploded in a certain place on a
certain day, and perhaps an assessment of the deaths and damage that resulted. Noting that
Hillary Clinton is the Secretary of State would be done only if her position was important to the
point of the cartoon. But this might betray the intent of the artist in the works in question. Thus, a
paradox: audience-focused records of images heighten the utility of records for that audience, but
diminish utility for other audiences.
The ease with which cartoons can be correctly interpreted has some bearing on their use
as historical documents, as per Weitenkampf (1946). This is not to say that there is any particular
level of certainty of subject that a cartoon could be held to; such things would vary from image
to image almost as a matter of course, with both topics and point-of-view potentially changing
daily. But those documents that are difficult to interpret – for any level of expertise in such
matters, and regardless of the amount of supporting documentation that can be found – would
seem to be poor candidates to illuminate the thoughts and feelings of the times on a given
subject. If this study is correct in stating that most people can correctly interpret editorial
cartoons most of the time, then Weitenkampf was right: these images are historical documents. If
this study is wrong and cartoons are difficult to interpret – not because they are obfuscatory or
obtuse, and not because such images may be contextually dependent, but because the right and
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proper interpretation of a cartoon cannot truly be pinned down – then their inclusion in the
historical record is questionable.
5.1.3 Similarities to Resources
Leaving aside sources that included editorial cartoons as an attraction or as a side issue to
something bigger, we can see that the findings of this research mirrors some of the characteristics
of those books and websites that could reasonably be counted as resources, where some effort
had been made to organize and provide access to the images based on something other than date
or author. Trudeau (1998) provides access to his strips by subject and by character, among other
things, and defines his subjects using jargon from the time or from the headlines that surrounded
the issues being examined. This is mirrored in the findings of this research in the use of
CONTENT/STORY on a fairly regular basis (corresponding, in this case, with Trudeau’s “subject”),
as well as a more often seen use of PEOPLE (corresponding, at times, with “character”). Similarly,
Cagle (2009) groups cartoons by subject as well, again corresponding to CONTENT/STORY, and
usually to the specific Class attribute Event. Both encompass broad definitions of what subject is,
as both occasionally bridge the gap between the event or story that inspired the cartoon and what
we might traditionally call the subject or subjects of the images. Brooks (2011) also groups
cartoons by subject, but does so on a yearly basis rather than an event-by-event basis because his
work is an end-of-the-year review instead of an ongoing effort to catalog the cartoons. His use of
subject best corresponds to the attribute Theme in the Class ABSTRACT CONCEPT, although it
occasionally crosses over into the realm of CONTENT/STORY. In all of these, one-to-one
conceptual relationships seem to exist between the native uses of the term “subject” and several
of Jörgensen’s Classes.
But the Mandeville (2009) collection of Dr. Giselle’s work and the work of Bachorz’
(1998) class concerning FDR-related cartoons go beyond this. Giselle’s work is rightly described
by date, as the dates that are depicted in those images can be of some import. But access is also
provided by what he terms “issue” (equivalent to “subject”), by “battle” (equivalent to “event”),
and by person. While users cannot search the collection using multiple terms, they can use
several different access points to get at appropriate cartoons (the metadata is hidden from the
user, making further analysis difficult at best). The Bachorz work allows, in some cases, for a
nested search, first by issue, then by date, and then by several key words and phrases that can be
viewed before the image itself. In so doing, it allows users to effectively and accurately narrow
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their search, more so than in any other reviewed resource, and to do so along the lines found in
the simulated query activity in particular, where ABSTRACT CONCEPT, LITERAL OBJECT, PEOPLE-
RELATED ATTRIBUTES, PEOPLE, and CONTENT/STORY—the first, second, fourth, fifth, and sixth
most-used of Jörgensen’s Classes (VIEWER REACTION being third, and inappropriate for database
retrieval) – are well represented.
5.1.4 Metadata
In her original 1995 work, Jörgensen asked participants to describe several images from a
catalog of potential illustrations, then used content analysis to group descriptions into 12 Classes
based on what aspects of the image they were speaking to. That is to say, the 12 Classes were
meant to describe only that set of image descriptions about that particular set of images, not to
set forth the be all and end all of image descriptions. In her 1996 and 1998 studies and her 2003
book, she further explored the efficacy of the Classes, but did not propose that the set of
descriptors was complete, comprehensive, universally applicable, or in any way representative of
the totality of how people describe images.
And yet several scholars, sometimes in collaboration with Jörgensen but often not, saw
the usefulness of the initial 12 Classes and used them as a template of image description
classifications, finding greater use of some Classes and lesser use of others when applied to
different sets of images. Over time, a set of results that could reasonably be compared to one
another emerged in the literature, and the various related works became a kind of metadata
schema for descriptive information about images, not in any formal capacity, but as a basis for
determining what users found to be important about a given set of similar images, and showing
differences in indexing needs between dissimilar image sets. Jörgensen never set out to codify a
complete list of things that might be described about an image, but several researchers treated her
12 Classes as if they were exactly this.
While Jörgensen’s 12 Classes were never meant to be a metadata schema, it can easily be
used to create one or as the basis for evaluating existing schema. When used as an aid in
creation, other needs and means of meeting those needs must come into play, as Jörgensen’s
Classes do not represent the complete set of indexing needs for most documents. Assuming that
the three basic kinds of metadata are operative – descriptive, administrative, and structural (as
per Caplan, 2003, and Gilliland, n.d.) – we can see that descriptive metadata is found in
abundance in the 12 Classes; there would be places for both generic and specific elements, for
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people, places, and things, for actions and for modifiers to any of these. In this realm, the 12
Classes do a reasonably good job of providing access to the individual items within the
collection it would describe.
While quite good in the realm of descriptive metadata, Jörgensen’s 12 Classes are not a
complete schema, and should not be treated as such. It was assumed as the Classes emerged from
Jörgensen’s research that administrative and structural metadata would be generated by other
means. Accepting that administrative metadata is that which deals with provenance, acquisition,
composition, and physical or electronic location, the only Class that would qualify here would be
ART HISTORICAL INFORMATION, where such attributes as Format, Type, Medium, and Artist are
recorded, dealing mostly with issues of composition and, to a smaller degree, provenance.
Structural metadata – that which helps to relate one item in a collection to the collection as a
whole – is not to be found in the 12 Classes, and rightly so; such issues are best left to the
overseers of a collection, as they can best anticipate, plan for, and tend to their own needs.
Similarly, Jörgensen’s 12 Classes can be used to evaluate certain kinds of metadata
within a schema. As discussed in Section 2.1.3.4, the Categories for the Description of Works of
Art (2011) was found to be the best extant metadata schema for the description of editorial
cartoons: it provides well for all three types of metadata, and is perhaps deficient for this specific
type of image because it does not provide well for the comparatively copious number of words
typically found in an editorial cartoon, nor does it provide well for recording the actions depicted
when applicable. We can dismiss the notion that WAYWARD TERM FAILURE might be covered in
such a schema because they are not designed to accommodate user-generated data that make no
sense. Likewise, we can dismiss COLOR, LOCATION, and VISUAL ELEMENTS as they were not
found to be useful Classes for editorial cartoons (although future research may find otherwise).
The Class ABSTRACT CONCEPTS would seem to be readily divided between the CDWA
Categories of “Subject Matter” and “Context”. LITERAL OBJECTS would generally fit into the
Category “Physical Description,” VIEWER RESPONSES into “Critical Responses,” and both
PEOPLE and PEOPLE-RELATED ATTRIBUTES would fall into “Subject Matter” as well, although
somewhat awkwardly.
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Table 32
Comparison of Jörgensen’s Classes to CDWA Categories Jörgensen's Classes CDWA Categories
Abstract Concepts Subject Matter; Context
Art Historical Information Object/Work; Classification; Creation; Materials and Techniques
Content/Story Subject Matter; Context
Description Descriptive Note
External References Related Visual Documentation; Related Textual References
Literal Objects Physical Description; Inscriptions/Marks
People Subject Matter
People-Related Attributes Subject Matter
Viewer Reactions Critical Responses
Note: Wayward Term Failure is not included due to its non-classifiable nature. Color, Visual Elements, and LOCATION are not used because they
were not found to be relevant in this research.
In addition to Jörgensen’s Classes, the CDWA also provides ample places for the different kinds
of administrative metadata, particularly dealing with issues of a works provenance and history,
and for the use of authority files for completing various elements within the schema.
Thus, the CDWA, as evaluated by this research in specific and by Jörgensen’s 12 Classes
of image description in general, is the best known extant metadata schema for the description of
editorial cartoons. But certain improvements could be made to the CDWA to accommodate the
idiosyncrasies of this particular type of image. Attention might be paid to the section
“Inscription/Mark,” particularly Inscription Type. It might be said that there are four different
kinds of text found within an editorial cartoon: the spoken word, written thoughts, labels, and
captions. Each of these might reasonably find a place within the subcategory Inscription Type,
which is defined as “The kind of inscription, stamp, mark, or text written on or applied to the
work (e.g., signed, dated, colophon, collector's stamp, hallmark)” (CDWA, 2012, emphasis
present). Actions within a cartoon do not readily find a place within the CDWA, probably fitting
best in the Category “Descriptive Note,” where textual or narrative descriptions of works of art
are to be placed. While this may be the best place for the description of action within an editorial
cartoon, it would not seem to provide the ready ability to group cartoons by, for instance,
“falling” or “fighting”.
In any case, the application of any metadata schema to large sets of images is a step in the
right direction, both for images specifically and for documents generally. As previously noted, a
vast majority of the editorial cartoons in the Library of Congress (2009) have only the bare
minimum of metadata to describe them, and the for-profit efforts of Trudeau and Mankoff, while
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more often described, are not more thoroughly described. It may be that such schema help to
fulfill Stam’s (1989) call for systems of description that allow for both autonomy on a per-
collection basis so that local needs can be met, while also providing for inter-collection
exchanges of both images and surrogates. Similarly, Trant’s (1993) emphasis on interoperability
between disparate systems – not unity of schema, but crosswalks between them – might be at
least partially met by the adoption of a schema such as the CDWA for the description of editorial
cartoons by providing a means for readily comparing the efforts of two or more collections to
help ensure a basis for comparison between records, leading to more accurate surrogates for the
images in question.
5.1.5 Folksonomies and collaborative technology
A folksonomy of a sort was used in the collection of data for this research. The ability to
collect data via the Internet was of great convenience, both for the researcher who did not have to
travel to static physical sites in the hopes of getting passers-by to participate, and for the
participants who could perform the tasks at their convenience, even stopping in the middle of the
task and returning later. In the same vein, the electronic presentation made for electronic results,
the raw form of which allowed for relatively swift and easy analysis, a great boon for the
researcher. Traditionally, the data gathering performed here would have been done in a face-to-
face format, perhaps on an individual basis, perhaps on a many-to-one basis, which, while
allowing for the researcher to observe the activities more intimately and perhaps generating a
somewhat more rich set of data because of it, would have been much slower, more difficult, and
very much more time consuming.
One problem that came about because of the electronic nature of this research was the
rise of the Class WAYWARD TERM FAILURE. Here, the physical and chronological difference
between the researcher and the subjects disallowed the timely and immediate resolution of terms
that seemed to make no sense when describing a given editorial cartoon. Granted, such problems
could have been resolved electronically as well, perhaps with an email from the researcher to the
participant, but such instances of confusion were not anticipated beforehand and thus were not a
part of the protocol, and in any case would have lacked the immediacy necessary for a true and
accurate answer from the participant.
Another difficulty encountered with the electronic data gathering in this research was the
nature of the program used to gather that data and the researcher’s reliance on expert assistance
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in properly setting up the program and in accessing the data. As originally written, the
steve.tagger (2008) program was inadequate to the tasks in the research; it asked for far too much
personal data from the participants, the welcome and instruction pages need to be modified to fit
the specific tasks and requirements of this research, and the post-task pages need to be modified
for each of three different end points in the activities. While these changes are not on par with
creating such a program from scratch, some level of expertise in HTML and PHP was necessary
to accomplish these goals, expertise that the researcher did not already have. This led to a
dependence on outside assistance that, while as timely as possible, was sometimes an
inconvenience to the researcher in terms of allowing the work to proceed apace. Similarly, access
to the electronic data required skills in SQL, skills the researcher also did not have and, when
those skills were inaccuretly thought to be acquired, led to considerable delay in the proper
analysis of the data. While the presentation of research material to participants electronically,
and the gathering of data from participants in a similar manner, are quick and convenient when
compared to traditional data gathering methods, they are not without problems and pitfalls.
5.2 Implications
5.2.1 For society
Several portions of society could benefit from this research and its implementation. Most
obviously, those in education might be able to use an editorial cartoon collection based on the
ideas set forth in this research, as it would allow access to time- and event-specific images that
would rightly be expected to reflect the feelings of at least a portion of society about a given
issue at the time the issue was relevant. Sometimes history can be presented in dry and
unexciting ways, ways that deny human or social factors in historical issues. Editorial cartoons
can provide the color and depth that text-based records of history sometimes lack.
Likewise, historians and political scientists could benefit from this work in that new
resources, geared toward the description of editorial cartoon content and less focused on
technical particularities and issues of provenance, would enable faster and more accurate
searches of large cartoon collections to take place, matching wants and records more efficiently
than currently possible in most such corpora. As previously stated, most collections of editorial
cartoons lack a method of image description that allows for easy access to the images within
based on well thought-out references to the historical events in question. In a similar manner,
most of the words contained in a cartoon are not easily provided for in most descriptive schema
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and systems. A careful consideration of the factors outlined in this research would help to
alleviate these lacks, providing a basis of description that would allow researchers to access these
works of historical commentary more quickly and certainly.
Beyond this, if we accept that any society that purposefully forgets its past is doomed to
repeat it, and we further accept the librarian’s axiom that an item misplaced or misdescribed is an
item lost, then we see that the continued lack of surrogation of and access to editorial cartoons,
especially when such access can now be had, is tantamount to simply throwing away parts of our
history. As a nation and as a people, we owe it to ourselves to keep track of these historical
documents, to not forget the feelings and concerns and fears of those who lived through notable
events, to remember that there are few things in this country that go unopposed and that this
opposition has a right to voice it. To preserve editorial cartoons is to preserve our history, and we
ought not doom ourselves to the needless repetition of it.
5.2.2 For library and information studies
The contrast between the results of this study of editorial cartoons and similar studies that
used different kinds of images clearly shows that, as a field, we have not studied a diverse
enough set of images. To draw conclusions about all images from studies that concentrate on
those that fall more towards Fidel’s data pole (images as visual records, devoid of further
meaning) is ill-advised as it leaves out a number of concerns that manifested themselves in the
results seen in this work, which show that there are times when the message contained within an
image is at least as important, and sometimes more important, than the parts of the image.
To alleviate this, we must first develop a method of discerning whether images rightly fit
on either of Fidel’s poles or somewhere in the middle. These methods could be based on the
ideas of Panofsky, Shatford, Fidel, and others as deemed appropriate. Efforts to include other
pertinent fields of study such as art history and psychology could be made so that what is already
known about the use and perception of diverse kinds of images can be brought to the fore. In this
way, a set of considerations for the composition of test sets of images – either for the purposes of
diversity or with an eye toward a more focused set – can be reasonably accomplished via a
standardized set of considerations.
Once this is done, we will be able to compose sets of images that meet our particular
research needs, and we will be able to communicate to other interested researchers how we
composed the image set and how others might wish to follow the initial research on a different
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set of images. The advent of this notional system of image set description would allow for
multiple researchers to engage in similar research across a wide range of image types, improving
the efficiency of collaboration efforts or allowing a more orderly progression of follow-up
research to be done. Additionally, it would allow longitudinal studies to be conducted where
images test sets would be similarly composed while controlling for the passage of time and the
concomitant changes this might bring about in the base of potential participants in such studies.
That previous generations of researchers have tended to ignore images of meaning in
favor of studying images of record is not surprising. The foundational work had not yet been
laid, the technical infrastructure had not yet been developed and deployed, and the need for a
systemic method of image description had not yet been seen. None of these factors are currently
operative. The time for a review of which images we choose to describe, and for how we
describe a more diverse set of images, has come.
5.2.3 For editorial cartoons
At the beginning of this dissertation, it was stated that while access to images in general
has improved in the last 20 years, due to both advances in electronic storage and dissemination
and to improvements in the intellectual provisions of them, access to editorial cartoons has
lagged behind. This is certainly true, and several examples can be found in the literature review.
But the circumstances under which this can be found have also changed.
Where we once assumed that people would generally get the subject of a cartoon wrong,
we now find evidence that this is not so. While it is true that a Class of description (WAYWARD
TERM FAILURE) needed to be developed for this dissertation to give a place for terms that
participants stated had to do with a given cartoon but that the researcher could not make sense of,
the Class was, in the end, little used. While it is true that this research did not address the
accuracy of the various descriptors for editorial cartoons, the evidence seems to point to people
mainly getting it right instead of wrong. People are quite capable of capturing both the subject,
topic, or theme of a cartoon, as well as the tone or intent of the image.
Where we once assumed that collecting large sets of cartoons in one place would take
near Herculean effort, we now find that technology allows us both to assemble and access such
collections with ease. The issues surrounding the assembly of such collections are a thing of the
past because large-scale data storage is now both cheap and easy to get and use. Means of
connecting to such collections are similarly simple, both in terms of interface design and of
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connectivity over great distances. Methods for actually getting the work done are addressed in
part by social media, collaboration tools, and the sense of community given by the two together.
Despite the relevant copyright and legal issues that might need to be addressed, the evidence
seems to point to the fact that the means of editorial cartoon description and collection are no
longer a problem.
Where we once assumed that the description of a cartoon was “just too hard to do,” we
now find that this also is not so. The intellectual access to images is better now than it ever has
been. Formal methods of image description – the Art and Architecture Thesaurus, Cataloging
Cultural Objects, MARC21, and other such cataloging schemes – either apply directly to images
or specifically accommodate them. Less formal methods of image description, namely metadata
schema such as the VRA Core 4.0 and Categories for Describing Works of Art, allow less
trained but perhaps more interested people and organizations to create surrogates for the works in
their collections, and to exchange such data with other such people or groups, thus trading and
discovering best practices. Decidedly informal methods of description, particularly those found
in folksonomies, allow information professionals to hear directly from a community of interested
parties, and to either accept whole cloth the data they provide or to harvest and refine such data
for both the wants and needs and for the opinions of that community. The evidence seems to
point to people – professional, amateur, and lay – being interested in the description of images of
all kinds.
This research shows we have the tools to create large cartoon collections. We have the
means to describe such images and access them via multiple points. And we have an intelligent
enough pool of talent to work on the former and make it part of the latter. We can close the gap
between the description of editorial cartoons and other kinds of images. We can better remember
our history. We can add depth and color to otherwise shallow and colorless times. And we can
refine our practices for all kinds of images, especially those who show more than is seen.
5.3 Future Work
5.3.1 Corrections
Asking participants to use two different modes of description – categorical (tagging) and
interrogative (query) – are commonly used methods for image description because they allow the
researcher to see what the users want and think with minimal interference from the procedures
necessary for data collection. The electronic collection of that data is not flawed per se, though
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certain considerations should be made when employing the Web for such activities (discussed
earlier). The particular electronic components used in this study are at least workable and
certainly affordable, while fully-funded research may wish to take advantage of custom-made
applications for their research efforts.
No questions pertaining to the participant’s identification with a given culture or
language were collected. A lack of familiarity with the politics and history of the cartoons may
have led to mistakes in the tagging and querying phases of this study. Future work should control
for the possibility of non-native English speakers, and for those relatively untouched by
American culture and politics. Specifically, variables such as Native Language, Familiarity with
Event, and Historical Background might be implemented in future research to assess the
participant’s personal context for describing a given cartoon.
Some of the statistical analyses that are normally applied to research such as this were not
appropriate for use because the sample was not randomly chosen. While there are some logical
and logistical arguments to be made concerning samples being “random enough,” most studies
seek reasonably random samples so that chi-squared distribution, ANOVA, and (particular to this
study) Krippendorf’s alpha (2004) can be applied to the data, and the answers acted on with a
reasonable degree of certainty that they have been applied properly. But the choice to initially
proceed with a non-random sample of academic professionals in carefully chosen fields of study
was made to ensure that the data set that resulted from the research was rich; the subsequent
inclusion of what might be considered random participants in a previously unconsidered field
does not change this. As the results from this exploratory research have laid the foundations
concerning what we might expect from similar research, random sampling methods should be
used in the future to allow for more statistical analysis, and for more generalizable results.
Also germane to more certain results would be the use of more than the one analyst so
that true intercoder reliability measures could be applied. While it is true that a basic,
rudimentary review of two of the ten cartoon’s tagging and query data was undertaken, this was
to ensure that the rules of the 12 Classes were evenly applied, not to ensure any sort of
correctness within those tags. As mentioned in Section 3.5.1.1, it would have been inappropriate
for another researcher to review the entire dataset, as would be necessary to properly employ
Krippendorf’s alpha because of the circumstances under which this research was conducted, but
such circumstances would not apply to future research efforts, which could easily employ
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multiple analysts for a given set of data, both in how that data were parsed for analysis and in the
coding of the data itself.
5.3.1.1 Jörgensen’s 12 Classes Jörgensen’s 12 Classes of image description were
not meant to be the be-all-end-all for the types of descriptions that could be applied to images;
the Classes are meant to describe the range – not the proportion – of what can be described in an
image. Rather, they were meant to classify the descriptions observed to come from both freeform
descriptions of illustrations and, in a similar but separate vein, potential queries for those images.
As noted, several researchers have used these Classes to describe different sets of images in
either of these scenarios: news magazine photographs, informational and scientific diagrams, art
history class assignments, search engine query analysis, image library behavior, and so on, all in
an effort, it seems, to generate data in a valid and reliable way. While there are now different
standards for image description, specifically several metadata schema, the Classes pre-date these
and, having been used for a longer time, provide a larger dataset for comparison due to the
longevity and documentation of the work done with them. While the Classes were not created to
be a quantitative model of image description, they have become a model of potential qualities
that can be found within an image.
If we accept this as true, then it stands to reason that we might improve upon the utility of
the Classes by two methods: first, by examining the data from similar studies to determine where
changes might be made; second, to examine questionable data from such studies, to see where
clarifications or improvements should be made. More specifically, the former refers to those
Classes that seem to be used more often than others to see if a split in that Class is warranted,
and the latter refers to Classes that are shown to be difficult to use by researchers, so much so
that the data in them might be viewed skeptically. From these two points-of-view, we might be
able to change the Classes in such a way that they would be more suited to the uses they have
been put to; we might amend the Classes to better fit their current use, rather than that which was
originally intended.
Chief among the changes that need to be made are ones related to the Class LOCATION.
This Class, as found in the original literature, deals with the location of people and things relative
to one another within a given image: for example, “the woman is to the left of the cabinet and the
flowers are above her” contains two LOCATION descriptors, “to the left” and “above”. When strict
attention to the proper use of this class is paid, its use varies; targets of editorial cartoons found
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no use for this Class, where news photos count such descriptors in one of ten descriptions. There
are other studies – not included for comparison in this research – that use the term LOCATION in a
more generic or standard way, describing the location where a photo was taken or the type of
background in an image. When LOCATION is used in such a way, we occasionally see an
explosion in its use, particularly when websites such as Flickr, a photo sharing site, often include
such relevant items as where a photo was taken, which fits properly in the Class
CONTENT/STORY with the attribute Setting. One study found that the Class LOCATION was used
over 40% of the time in such a situation (Rorissa, 2010). While this may be true in the more
vernacular use of the word “location,” it is difficult to imagine that this state of affairs would be
so if the prescribed use of the Class was properly applied.
While the relative position of people and objects within an image is important, the Class
should not be called LOCATION, but should instead be called POSITION, with the attributes
Relative (to describe relative positions, as in “to the left of”), and Place (a more absolute
statement of position within an image, as in “lower right” or “upper middle”). This new Class
would intrude on the attribute Activity in the Class CONTENT/STORY, subsuming it in part;
Activity would need to be redefined to include the actions depicted in the image but not the state
of the actors. In this, the position of a person or thing could be described in relation to other
things in the image, to itself, and to the image as a whole.
The Class LOCATION would be better used in describing those things that are more
commonly thought of with the word “location”. Setting and Background would certainly be
attributes of this particular Class, describing, for instance, the mountains that make up the
environment of a photo and the kind of background used in a studio portrait, respectively.
Geographic LOCATION would, as an attribute, accommodate both place names and the various
sets of coordinates that denote location. We might also include the attribute Scene to describe
common descriptions in an image, like “a bedroom” or “a parking lot”. Among these four
attributes, we would find that both the specific and the generic are accommodated, while both
showing relevant descriptions for images and keeping with the more commonly thought of
aspects of the Class name.
There is no good Class that describes a group of people, such as “horticulturalists” or
“Republicans”. The default for such a description is the attribute Social Status under the Class
PEOPLE-RELATED ATTRIBUTES, which is defined as “status of humans specifically commented
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upon, in addition to or in place of terms coded PEOPLE; includes occupation, race, nationality
(“upper class,” “Japanese,” “cab driver”)” (Jörgensen, 1995). While this may well describe the
social status of an individual found within an image, it does a poor job describing what makes a
group of people somehow related to one another, which proved to be a problem when so many of
the editorial cartoons in this study in particular consisted of “Democrat,” “Republican,”
“conservative,,” and “liberal” because such things are not a status and do not closely mirror such
things as occupation, race, or nationality, as these are more appropriately applied to individuals
instead of groups. In many cases, descriptions that applied to groups of people were used to
describe actors within the image, and as such the groups of people were acting as one person.
This research shows that while it is important to account for descriptions of a person’s group, it
is also important to allow for a group to be counted as a person or as a single actor within an
image. This would be easily solved by allowing for the attribute Group to be added under the
Class PEOPLE, and for the attribute Social Status to be amended to read “status of individual
humans specifically commented upon” (emphasis added), dropping the part that reads “in
addition to or in place of terms coded PEOPLE”. Later research (Stvilia and Jörgensen, 2009)
showed that the need for such a category was at least partially manifest in the tendency for
photographs of groups to need to identify the community that the images represented.
The allowable descriptions under the attribute Reference for the Class EXTERNAL
RELATION severely limited the use of this Class in this research because of the requirement that
the entities being referenced be either pronouns or proper nouns. For instance, one of the
cartoons referred to cultural norms surrounding the overarching issues of security and of
discrimination. Neither of these can be claimed as the subject, theme, topic, or focus of the
cartoons in question; rather, they were referred to in order to help set the scene. Both would have
been better described as an EXTERNAL REFERENCE rather than as a Theme under ABSTRACT
CONCEPTS, but since these are not proper nouns or pronouns, they could not be classified as
References under EXTERNAL RELATIONS. More pertinently, the differentiation between the
Theme of an image and a simple reference to something outside of it is not made clear. Could a
description be both? Certainly. But the determination of when a comment is one but not the other
is not made clear within the extant rules of use for the respective Classes involved. Amending
the attribute Reference to allow simple nouns would allow for such terms to be properly included
in EXTERNAL RELATIONS.
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This research produced a number of tags and query statements that could not readily be
classified, and were held out in the new Class WAYWARD TERM FAILURE. We might speculate
that most efforts such as the one represented in this research would produce a number of such
terms, descriptions that have no place because of the lack of context that sometimes comes with
tagging (see Section 5.2.2). This lack of place for such terms may sometimes lead to descriptions
being improperly or inaccurately classed within the 12 terms, subtracting from the validity of the
results. We might propose that a Class such as WAYWARD TERM FAILURE be included as a nod to
both the increasing frequency of tagging in research (and the resulting difficult-to-deal-with
terms produced) and as a Class for such terms to be placed without skewing the results of such
research efforts. While it is difficult to imagine the sort of tag and query analysis done here that
does not generate confusion on a small number of terms at least, none of the other studies that
have been compared to this research elevated them to a Class.
In this particular research, the inclusion of the attribute Text within the Class LITERAL
OBJECTS seemed inappropriate, both because of the dominance in the Class by the one particular
attribute in both phases of the research, and because it was conceptually a poor fit. As originally
conceived, this inclusion makes some sense: illustrations seem unlikely to carry text with them
as a matter of course, and there is no better fit for the attribute than under the Class LITERAL
OBJECTS. However, when the image set changed to editorial cartoons, we find that words are far
more prevalent: spoken words, thoughts, labels, and captions are all standard parts of such
images, and their inclusion in this Class made the comparison of results between similar studies
suspect, because it was a comparison of apples to oranges. Under certain circumstances, Text as
an attribute of LITERAL OBJECTS make perfect sense, but not when that text is such an integral
part of the message shown in the image, and not when such text is specifically being used to
clarify the meaning intended in the image itself. This research seems to show that the inclusion
of text as part of the background or as incidental content is rightly placed under the Class
LITERAL OBJECT, but that explanatory or expository text should be broken out into its own Class,
with the Class called TEXT, and the attribute name under LITERAL OBJECTS changed to Incidental
Text.
5.3.1.2 Heterogeneous image sets The use of homogeneous sets of images in image
description research is fairly standard. This is not to imply that random sets of images are not
used; it is to say that most images sets are somehow related. All the images used in this study
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were editorial cartoons. Westman and Hernandez used newspaper photographs. Rorissa used
travel photos. Jörgensen used illustrations for books. Those studies that concentrated on queries
for images rather than image descriptions found an analogous similarity, namely that all of the
queries within a study search for images under similar circumstances. Chen’s subjects searched
for art images as part of an assignment, Jörgensen’s in the aforementioned circumstances, and
the research conducted here pertained only to editorial cartoons. Among those who followed
Jörgensen’s research model, only Jansen had subjects who searched for images in general.
In performing research this way, we may be inducing a halo effect of some kind. It could
be imagined that the participant’s tagging activity for one editorial cartoon could easily carry
over to the next, producing results for the subsequent cartoons that are affected by the first
(Nesbitt & Wilson, 1977). This is easily dealt with in practice by ensuring that the order in which
a set of images is presented is randomized for each participant (as was done here and in the other
studies used for comparison), so that the first, potentially halo-inducing image constantly
changes, and the effect is ameliorated. But such a practice loses its effectiveness over the course
of several images; when the set is sufficiently large, such randomization practices lose their
punch. This is a circumstance that may also increase with the amount of time a participant
spends performing a task; if the tagging time for a set of images is long enough, certain kinds of
experimental fatigue can set in, leading to participants not applying the standards of practice to
the last images that they had previously applied to the first in a set (Aibing et al, 2002; Smith,
2001).
As part of an effort to confirm the findings in this study (and others like it), another round
of data gathering, following the same model set forth in this research, should be conducted, with
the only change being that the set of images used would not consist solely of editorial cartoons.
This brings to the forefront another question: what is a sufficiently random set of images?
Specifically to this research, what is a sufficiently diverse set of images? What factors need to be
considered? If we suspect, for instance, that there may be a problem with one set of results being
skewed too heavily toward Jörgensen’s ABSTRACT CONCEPTS, do we simply need to include
images that are seen as having little chance of this Class of description being applied to them? Or
do we need to try to account for each aspect of a given image description system’s elements
when creating a set of test images?
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On the surface, it would seem appropriate to mix the intended types of images in with a
random set of images, then separate the data for those images which are the object of the
research from the overall set of results, comparing the two for similarities and differences. This
would seem to solve the problem of the practices of tagging, for instance, images of kittens
might have on the practices in tagging editorial cartoons. The problem then is that whatever
emergent practices in tagging the intended images would show, some of those practices might
come from the repeated experience instead of from tagging the editorial cartoons specifically,
because they would have neither the time nor the environment to fully develop, leading to a
different kind of loss.
Which, then, is to be preferred: a singular, focused set of images that might yield that
skewed results through continuity and habit, or a randomized set of images that contains sample
of the focus of the research, that may fail to yield practices and options that only occur with
repetition and exposure to type? It is difficult to say. In any case, it is by no means certain what
effect the homogeneity or heterogeneity of an image set has on the tagging results for such sets;
more research into this specific issue needs to be conducted and published, so that a more
universally applicable and more fully informed research environment can be created for all
interested parties.
5.3.1.3 Confidence in tags The ability to ascertain the participant’s confidence in the
data he is providing – to gauge how sure he was that he was right – was wished for on several
occasions by the researcher. In some cases, this was because the research found that the tags
were out of line with the other tags or parts of the query in question; the attribute Theme was
commented on more often than any other single attribute, but often left the researcher wondering
if the participant was sure that this was the answer they wanted to give. Some sort of confidence
measure, perhaps a Likert scale of some sort (Stvilia & Jörgensen, 2009) should be included in
future research efforts that follow the same basic methodology described here. While this would
not allow researchers to ensure any sort of correctness in a tag or query term, it would allow the
researcher to discover if the participants are certain that their answers are correct, or are perhaps
tentative about their thoughts about the images. We would then be able to determine if certain
Classes of description generate some trepidation among those tasked with describing editorial
cartoons (and thus in need of more attention to detail in, for instance, the rules in a metadata
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element), and if other Classes more naturally conform to already extant or intuitive rules
regarding inclusion in that Class.
5.3.2 Supplementary studies
While these research efforts would help to build a solid foundation, there are some other
issues that would add to the general body of knowledge in other areas, one that grew from gaps
in the literature but was not addressable in this research, and another that emerged from the
research as a post hoc question. Future research efforts could address these issues to help
highlight potential pitfalls in the methodology, or to guide practitioners in the implementation of
descriptions for editorial cartoons.
5.3.2.1 Effect of time on cartoon interpretation Chappel-Sokol (1996) found that
many of the editors that she interviewed as part of her research thought that the demand for
reprints of editorial cartoons rapidly diminished with the passage of time because the relevance
and immediacy of the images faded so quickly. Previous efforts by Landbeck (2002) with the
Claude Pepper Collection revealed that when a cartoon several decades removed from the event
depicted, that lacked references to events or people not already known to the researcher, took
more than ten times as long to properly describe as did images where such data was known
beforehand. But little research has been done that determines the “shelf life” of editorial
cartoons, that seeks to see how long after its publication the subject of a cartoon might remain
known or knowable to a given participant. Knowing this could help lead to outlining different
strategies for the description of older cartoons, and may also show that the determination of what
might be important in the description of such images changes with the age of the image.
Initial efforts for this research might begin by imitating the tagging parts of this study,
presenting recent cartoons to participants for tagging without the use of any guidelines. The
timeliness research would then proceed to present the same cartoons to entirely different
participant groups at regular intervals, perhaps two, four, and six months, with participants
performing the same tasks under the same conditions. The number of tags, the composition of
those tags, and the accuracy of those tags could then be determined for each of the groups, and
similarities and differences noted. In this way, we could see if the subjects of editorial cartoons –
shown in this research to be the most important aspects of such images – are recalled or
determined by users when chronologically removed from the inspiring event.
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5.3.2.2 Effect of time on recall Related to but separate from this is the need to know
what aspects of editorial cartoons are recalled over time, which is to ask what features of such
images are remembered by research participants sometime after the cartoon is first viewed. This
would be another venue for description: to see if the tags and queries that a person writes about a
cartoon today are the same as those that he uses for describing and searching some months after
the initial viewing. To research this, participants might be asked to work with editorial cartoons
much as they have in this research, with the addition of being able to opt in to participating in a
third phase, one that would ask them to recall the images, without the image in front of them, and
to have them describe the images and query for them again. If we were to compare the frequency
of use of the 12 Classes to the before and after for both activities, we could see which aspects of
the images lingered on in the minds of the participants, certainly enough to see if the images
were recalled at all, and perhaps enough to see if the most dominant aspect of the images, the
attribute Theme in the Class ABSTRACT CONCEPTS, remained as the most-used descriptors of the
cartoons.
5.3.2.3 Personal agreement and describing behavior While this research asked
for participants to self-identify their political leanings, it did not seek to determine if they
happened to agree with the point or points the cartoons were trying to make, and what effect this
may have had on their tagging or query behavior. While we might guess that the message of a
cartoon might be in conflict with a given political school of thought, it would only be a guess,
and would not take into consideration individual variations or deviations from those schools.
Asking this of each participant for each cartoon is a simple matter, either electronically or on
paper; a simple check box would do in either milieu. Analyzing the behavior along lines of
political philosophy and interpretation would allow us to see the degree to which personal bias
might determine the tagging and query behavior in editorial cartoons.
5.3.2.4 Supplemental data While the interviews shed some light on the research
questions, they also yielded a great deal of supplemental data, things that did not have anything
to do with the research questions specifically, but did have to do with cartooning in general, and
that might serve to better inform the researcher as to which questions should be examined in the
future. Interviewee #1 allowed that her job was not to catalog or index, but to help people find
images in various resources, and as such her job was made easier by knowing both how
particular records are built and what users are looking for when they search for images. All of the
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cartoonists stated plainly that there is no difference between a political cartoon and an editorial
cartoon; Interviewee #2, in response to a question about which word to use when speaking to a
group of cartoonists, stated “No one I know [would make a big deal about it]. No one would say,
‘Oh, but you’re talking about the other guys.’” The cartoonists also believed that most of the
single panel cartoonists (as opposed to those who produce strips) do their own lettering and
inking, where strip art tends to be the work of three different people, each with their own role.
Two image professionals in large federal document management agencies stated that the use of
the best metadata schema – regardless of origin – tended to be used in initial descriptions of
images, and that a crosswalk between whichever schema was used and the traditional system of
description was developed in-house. Two of the three cartoonists vociferously voiced the belief
that they are the same as columnists, differing only in the medium they use to express their
opinions. All of these have to do with the theory and practice of editorial cartooning, but none of
them have anything at all to do with the research questions at hand, and serve only to better
inform the researcher in future endeavors.
5.3.3 Practical application
As previously mentioned, the Library of Congress (2009) has the largest collection of
editorial cartoons in the world, some 60,000 images both historical and contemporary, both
American and foreign. Of these, perhaps four or five thousand have in-depth, complete
surrogates that describe them in detail; the remainder are described primarily with information
regarding date of publication, creator, current physical location, and date of acquisition. This is
not meant to heap blame on the Library of Congress; among other things, limited resources in
terms of properly trained staff and a lack of both funding and impetus for creating a fuller record
of what, exactly, lies within the collection of cartoons has brought about the current state of
affairs.
How might the research described here help to solve this problem? Library and
information sciences are applied sciences that solve real world problems, and the aforementioned
problem certainly qualifies: it falls squarely in the realm of such academic endeavors, and
several research lines within the discipline can shed light on how this particular problem might
be solved. In addition, the academic realm in general provides fertile ground both for the
theoretical and the practical issues in solving such a problem, potentially making academia the
ideal place to solve the problem of accurately describing the subjects of these cartoons, and
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converting such descriptions into catalog entries. The following is one possible scenario of how
this could be accomplished.
If the overall problem here is the description of editorial cartoons, then the problem is
twofold: the method for description, and the application of that method. To resolve the lack of
workhours that can be dedicated to this effort, graduate students in the fields of library and
information science, political science, history, and other related fields could be asked to research
a number of editorial cartoons for the Library of Congress in exchange for credit, perhaps as part
of an internship, directed independent study, or in a formal class, in which they would do the
work of fully describing, say, 20 cartoons over the course of a typical 16-week semester. The
class could be run online and asynchronously (or both), with a syllabus that outlines the
expectations of the class regarding what constitutes quality work, what will happen with the
work produced, and other such issues that are best dealt with beforehand.
Previously, the CDWA was described as the best metadata schema for the description of
these images, and the Library of Congress has the world’s largest collection of editorial cartoons,
but has not yet described more than 10% of those images in detail. The Library of Congress
naturally uses MARC 21 to describe its holdings, everything from musical instruments to
historical documents to books to cartoons. But MARC21 would be an inappropriate method for
interns to use in describing editorial cartoons because of the complicated ways in which it works,
and the esoteric (to those outside the library field) nature of how the various rules of MARC21
might apply to such images. Additionally, using MARC21 as the blueprint for describing
editorial cartoons would be difficult because, at its base, it is not meant for the description of
them; it is not the first but the last word in the description of all of the types of holdings in the
Library of Congress, and as such is not the best choice to begin the process of describing a
particular set of images for non-library professionals. An intermediate step, designed specifically
to describe editorial cartoons, is called for, specifically, a metadata schema, one that is easier to
use than MARC21 and more forgiving in the rules for the use of its various elements.
A modified version of the CDWA, based on this research, would fit the bill nicely. It
would need to be modified in two ways: removing the structural and most of the administrative
metadata elements (or at least not focusing any great effort on them), and more sharply focusing
the remaining descriptive elements to address the needs found in this research. The first would be
necessary to remove distracting elements of description from the list, a problem when in novice
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hands. The second would be necessary to help guide the collection of data that is missing from
the image surrogate but accessible in the general research corpus, that information which can (it
is hoped) be derived from the historical record and put in a form that would be readily usable by
the Library. It would also be vitally important that each point of information given about the
subject or subjects of a given cartoon be properly referenced, so that the veracity of each record
can be ascertained. A two-crosswalk conversion between the CDWA and MODS (Getty
Research Institute, 2012) and MODS and MARC21 (Library of Congress, 2012) would complete
the transformation of data into a useful form.
The course would begin with assigning two or more such students to each cartoon for a
set of cartoons, sending a randomly selected set of cartoons to each intern from a pool of images.
At first, each record from each student will need to be checked both for accuracy and for
adherence to the rules given for each element, so that any changes to the schema can be proposed
and implemented. The Library will need to compare and contrast the records for each of the
cartoons, either picking the best one or creating hybrid records using the best parts from several
records. Once the processes and practices are established, the crosschecking for accuracy can be
made into another internship opportunity, perhaps drawing on the best performers in the initial
indexing tasks. As the records are created, crosschecked, and approved, the conversion of the
information in them to MARC21 records would begin. This process would continue until all the
cartoons are properly described, discontinue when there are no more cartoons to be described,
and would be revived when the collection of cartoons is expanded.
5.4 Conclusions
5.4.1 How do the tagging terms compare to the querying terms?
The results of the tagging activity and the simulated query activity are so similar that
listing the differences would be counterproductive; even though a non-random sampling method
was used, it is reasonable to conclude that the differences in the results between the two activities
in this research fall within a notional margin of error, rather than to attribute the differences to
the activities that took place. In Figure 29, we see that not only do the activities produce the same
pattern of results among the 12 Classes, we also see that the greatest difference between
activities is about 5 percentage points, with other results being almost identical. In Section 4.3.1,
the examination of the largest differences shows that, for the most part, these differences make
sense within the context of the respective activities. For editorial cartoons, tagging terms
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compare to querying terms similarly; they resolve in much the same way within Jörgensen’s 12
Classes.
This similarity in the results between these two parts of this study brings into question
Bates’ assertion (1998) that the experiences of the indexer (in this case, the tagger) are
phenomenologically different than those of the searcher (in this case, the querier). While this
may be so within the experiences of the individual, it would seem reasonable to assume that such
experiences would manifest over the experiences of several participants in different results in the
different activities within this study, but this is not the case. That the overall frequencies of use
of each of the Classes parallel each other so closely between the two activities seems to indicate
that there is no difference in the experiences of the indexers versus those of the queriers. But as
seen in Section 5.2.3, there is some question as to whether the expected differences in
phenomena might have manifested in some unexpected ways.
5.4.2 How are editorial cartoons described in a tagging environment and a
simulated query environment? And how do those tags fall into Jorgensen’s 12 Classes of image description?
5.4.2.1 Among similar studies Because of the similarity in their respective results,
there is little to be gained from discussing the two original research questions separately. Thus,
unless otherwise specified, they will be discussed together, and will be separated only when the
division between the two activities is large enough to warrant examination, and when this takes
place the discussion will plainly separate the results of the tagging activity from those of the
simulated query activity. Otherwise, results will be discussed in approximations, to encompass
the results from both.
In the tagging activity, the use of the Class LITERAL OBJECT is fairly steady throughout
the five cited studies and this research, but this may be deceiving. Most of the uses of this class
in the research stemmed from the inclusion of the attribute Text; without it, the frequency of use
for LITERAL OBJECTS drops from about 19% to about 4.5% in both the tagging and the query
activities. Such a distinct and dramatic use of a single attribute with this Class was not found in
any of the other studies. That such a difference should be found between editorial cartoons and,
say, scientific diagrams, makes sense, but that the same margin of difference is found between
these images and news photos is somewhat surprising. This would seem to indicate that it is
unusual for an object in an editorial cartoon to be considered important in understanding its
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point, and that whatever text is found within a cartoon might be found to be important in both the
cartoon’s description and in queries for the image.
COLOR was occasionally noted in the other five studies, but was not noted at all in this
research, even though nine of the ten cartoons used were in color. Granted, the original drawings
from the various creators of the cartoons were in black and white, with color added to the
electronic versions of the cartoons afterward, and as such color was not part of the intended
message, subsequently giving such a Class of description less importance. Still, that color should
be present in most of the images yet not counted as a “key phrase or word” (as asked for in the
tagging task) seems odd.
That the Class PEOPLE should be considered important in describing editorial cartoons is
not surprising in the least; generally, such images examine actions and events involving certain
people, and would thus seem to be an integral part of describing the image. The cited study
involving scientific diagrams makes little note of people (as we might expect), but the other four
studies and this research find that such aspects of their respective images makes use of this class
of description. Oddly, three of the ten cartoons used in this study show no people, yet even these
cartoons make about the same use of this Class as do the other images. In the other five studies,
only Jansen found that PEOPLE were part of a query more than 11% of the time, finding the Class
used 30% of the time in queries for images in a sampling of excite.com image searches.
LOCATION – used in Jörgensen’s Classes as the indication of relative position of people or
objects to one another within the image, with Setting used as an attribute of the Class
CONTENT/STORY to denote the typical “where” aspects of an image – was used when describing
illustrations and news photos, but rarely in scientific diagrams (which is not surprising) and not
at all in editorial cartoons, which is surprising. It may be that the method of description –
narrative for the illustrations, tagging for the cartoons – may have hindered the use of the Class
of description in this research. Most other studies found that LOCATION was used between four
and eleven percent of the time, except for Chen, who found that it was used over 32% of the time
when students were searching for images as part of an assignment.
Among the five cited works and the current work, it is this one that produced the smallest
proportion of the Class CONTENT/STORY, which is something of a shock considering the nature
of the images in question. We could have assumed that such things as Setting, Activity and Event
(which are some of the attributes of this Class) would have played a larger role in the description
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of editorial cartoons, but more than one overall description in three instead dealt with the themes
and topics of the images, leaving the aforementioned subtextual elements far less represented in
the results. Oddly, CONTENT/STORY was found almost twice as often in the query activity than in
the tagging activity, 8.2% of the time as compared to 4.8%. For this type of information about an
image to be considered important is within expectations; after all, the “what’s going on”
information about an image is at least as important as the who and the what of the image content.
But for such data to be considered more important in a query than in a description is mildly
surprising.
The Class VISUAL ELEMENTS refers to the composition of the image in terms of its
nonspecific constituent parts using attributes such as Shape, Texture, and Perspective, among
others. While the other five cited studies found some use for this Class, this research found no
tags that would reasonably fall into this category. As previously mentioned, it seems that the
message that the cartoons were perceived to be sending was more important than what parts
make up the image, seemingly continuing the general finding that the bulk of the description of
editorial cartoons centers on what is being said, and not on the methods used to say it.
DESCRIPTION was rarely used by participants when describing editorial cartoons, but was
used to some minor degree (with the exception of Jansen, who found it to be the most-used of the
Classes) when describing the illustrations, diagrams, news photos, and queries found in other
studies. Composed mainly of quantitative description and by the use of adjectives, this Class of
description was used less than a third of a percent of the time in this study, perhaps because of
the aforementioned focus on the intended message. But this does not explain the lack of
descriptive material concerning the tone, severity, or perceived misguidedness of the various
messages to be found in the sample cartoons. The comparative lack of use of this category is a
surprise to the researcher, even with other attribute/Class combinations, like Atmosphere in
CONTENT/STORY, taken into account.
The Class PEOPLE-RELATED ATTRIBUTES was used fairly often, mostly to describe the
perceived political affiliation of the people or symbols in a cartoon either in the generic
(conservative/liberal) or in the specific (Democrat/Republican). That this Class of description is
found more often for editorial cartoons and newspaper photos than it is in illustrations and
diagrams is no surprise at all; we might expect that the characteristics of the people in the former
are more important to understanding those images than would be so in the latter. But that the
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proportion of PEOPLE-RELATED ATTRIBUTES to the number of PEOPLE mentioned in the
descriptions of the former should be so consistent in the former – about 8:7 for both – is a mild
surprise, especially when compared to 1:3 ratio of Jörgensen’s findings for illustrations. Of the
12 Classes, this is the most steadily used among the eight scenarios among the six total studies.
The Class ART HISTORICAL INFORMATION showed different frequencies of use across the
four tagging studies, running from 0% for news photos to 12.8% for image searches for art
history assignments. Why this is so is beyond the ability of this research to reveal, mainly
because we cannot ascertain the frequency of attribute usage in the other studies. But for this
study, the use of this Class clustered around the author’s name, the publisher of the cartoon in the
tagging activity, and around the fact that the images were, in fact, cartoons in the query activity.
On the one hand, taken together, this points back to the previous findings of Landbeck that
showed a consistent reference to the words in a cartoon when trying to describe it. On the other
hand, the use of this Class of description is 1.8% for tagging and 6.3% for querying, as large a
gap as was found between any two Classes in this research, a difference mainly accounted for in
the query activity in noting of the image being a cartoon coupled with the abandonment of noting
author and publisher in the same phase.
That VIEWER REACTION should be used far more often for editorial cartoons than for the
other types of images used in the other studies is no surprise at all; part of the point of such
images is to elicit reactions of some kind, and it is considered a mark of achievement when a
cartoon does so. That this Class of description should be the third most-used Class is unexpected,
as it was assumed that PEOPLE and, perhaps, PEOPLE-RELATED ATTRIBUTES would be used more
often. The frequencies found may be, in part, due to the assumed high quality of the cartoons in
question; as the authors of all the cartoons are recent Pulitzer Prize winners, it can be assumed
that their present works are of similar quality, and thus more likely to provoke such reactions
from the study’s participants. While 14.5% of the tagging data was Personal Reaction, a
surprising 10.1% of the query data was also Personal Reaction. For this to be so for the former
makes a lot of sense; it was, after all, the more open and less directed of the two activities in this
study, and as such should find a large number of personal reactions among the descriptions. But
that participants generated one search term in ten in the query activity as a Personal Reaction
defies reason. It is difficult to imagine the search engine that would give relevant hits to a query
that included descriptions of personal reactions, even for editorial cartoons. Perhaps participants
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lost focus on the task in the query activity, slipping in personal reactions instead of focusing on
query terms over the set of ten images. Perhaps the use of technologies associated with
folkonomies induced behaviors the participants brought with them from previous tagging
experiences, behaviors that included a sizable dose of personal reactions to the ionformation in
question. In any case, their inclusion in the data for the query phase of this research is a surprise.
We might wonder if the unusually high use of the Class ABSTRACT CONCEPT, especially with the
attribute Theme, might have something to do with the high use of this class; perhaps the
evocative nature of the Themes of editorial cartoons led to Personal Reactions found in this
study, but this connection cannot be determined with the data generated in this research.
The Class EXTERNAL RELATION was the most difficult one to assess for editorial cartoons
because of its conceptual proximity to the attribute Theme, which falls under the class ABSTRACT
CONCEPTS. The theme of a cartoon was often seen as several degrees removed from the people
and objects that composed the image, but just as often such descriptions were not a statement of
Similarity or Comparison (two of the three attributes for the Class), and the Reference attribute is
restricted to proper nouns and pronouns only, leaving a number of references to generic objects,
institutions, and locations not pictured in the cartoons no other place, but Theme. But the
comparatively high number of EXTERNAL REFERENCES in the editorial cartoons as compared to
the news photos is something of a surprise not only in the disparity of the numbers, but in and of
itself. Given the high incidence of tags falling into the ABSTRACT CONCEPTS Class, we might
have expected that EXTERNAL RELATIONS – a conceptually similar measure – would have been
more frequently used. Compared to the other queries, the use of External Relation is quite high,
as most of the other such studies found the Class used less than 1% of the time, but the number
found here matches closely to the other tagging related studies.
By far, ABSTRACT CONCEPTS was the most often used Class for the description of
editorial cartoons. As mentioned it was used four to 22 times more often when compared to the
other cited studies. Nothing in any of the literature indicated that this particular Class would be
used as often as it was. Combined with the disparity between the findings in this study and those
in others, this was easily the most surprising of the results in the research. Why? Because all the
other research efforts that followed this general type of methodology – all of them – found that
participants spent far more time talking about the medium, where the research here found that
they spent most of their time describing the message. Which is to say, the other research found
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that participants spent more energy describing either the constituent parts of the image (as in
LITERAL OBJECT, PEOPLE, and LOCATION) or the visual components of the image (such as
COLOR, VISUAL ELEMENTS, and ART HISTORICAL INFORMATION), but the research presented here
found that the participants describing editorial cartoons spent their energy describing the point
that the cartoons were perceived to make, mainly in terms of ABSTRACT CONCEPTS and VIEWER
REACTIONS.
In the tagging activity. we might expect that images as different as Jörgensen’s
illustrations and editorial cartoons would produce different results, but the overall results were
also quite different from Laine-Hernandez & Westman’s results (2006) when applying the 12
Classes to photos from a news magazine. Surely some differences can be attributed to the
editorial cartoons being aimed at and tagged by American audiences, while the news
photographs were of and for Finnish readers. That the cartoons produced more VIEWER
REACTIONS than did the photos makes sense, as does the far larger frequency of use of
ABSTRACT CONCEPTS for the former than for the latter (after all, cartoons are about issues, while
pictures are about events). But we might find that the larger number of references to
CONTENT/STORY for the photos as compared to the editorial cartoons is a surprise, perhaps
stemming from ABSTRACT CONCEPTS for editorial cartoons being dominated by the attribute
Theme, a notion that could be seen as overlapping with the Class CONCEPT/STORY. The absence
of use for the Classes COLOR, LOCATION, and VISUAL ELEMENTS for the description of editorial
cartoons is also surprising, particularly in the light of how often they are used in the description
of other types of images.
The differences in the overall findings among the query-based studies follow the same
sorts of patterns, for the most part. As noted, there seems to be more dissimilarity among these
studies, a greater variation in use among the Classes than found in the tagging activity. But the
main eccentricities for editorial cartoon queries – heavy usage of ABSTRACT CONCEPTS,
surprisingly high use of VIEWER REACTIONS, and the dominance of LITERAL OBJECTS by Text –
are as true of the queries as they are for the tags.
One of the things that is similar among all of the previous studies and the work done here
is the idea that to describe the image in question, the story or narrative of the image must be
captured using text. Whether the image in question refers to events past and potential action in
the future (like editorial cartoons) or is meant to record the visual outcome of a given event (like
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a newspaper photo), whether a query is meant to fulfill the requirements of a school assignment
(as per Chen) or simple curiosity (as per Jansen), the thrust of the description for each image is to
capture the salient points of the story being told in words, not to replicate the image in verbal
form but to create a reasonable surrogate for it. Thus, despite dissimilarities in the manifestation
of how this is accomplished, a unified effort among disparate images is evident.
5.4.2.2 Among dissimilar studies How do the results of this research compare to the
findings of studies that used different methods for commenting on image description? Armitage
and Enser (1997) found that the most often sought aspects of image queries in British libraries
were of specific people and places and generic people, but while people were a much sought-
after aspect of editorial cartoons, it was not the most-often sought. They also found that that both
of these vastly outnumbered requests for abstract things, aspects certainly not echoed in this
research, where ABSTRACT CONCEPTS (a reasonable comparison between the two) constituted the
bulk of the requests made for the cartoon overall. Greisdorf and O’Connor (2008) found that the
focus of image descriptions was not the content of the image but the context, an analogous
finding to that of this research where the message was found to be more often described and
queried for than were the items and people found in the images. Hollink, Schreiber, Wielinga,
and Worring (2004) found the users described and queried for the more abstract aspects of
images more often than found in other studies, but also found that, overall, the constituent items
within an image were the most-often sought in either case, a kind of middle-of-the-road finding
when compared to the results of this research and those of others.
In this, we see that there is a great deal of speculation about what to expect from users
when they are describing or when they are searching for images in the electronic age. Different
researchers have used different methods and come up with different results, failing to find any
unanimity in even the most general terms. Largely, these researchers have used different types of
images in the various describing tasks, and different situations for searching in the other tasks.
What can we make of this? Certainly, it is not a call for unified methods of image research, as
the diversity of findings found here and throughout the literature, while adding to a network of
conflicting findings, also shows the directions that legitimate image research should be heading.
Neither should we attempt to isolate any particular kind of image, as this would lead to a
furtherance of the academic problems involving researching being an inch wide and a mile deep,
which does not much help the end user in their tasks. But we do find that the same questions
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seem to be asked over and over but in different ways, those being: how should images be
described, and how do people search for images? And in this, we should seek not to close ranks
and find the smaller, more exceptional truths, but should instead seek to further expand the study
of the types of and situations in which images are used, in an effort to bring the full gamut of
practices and preferences into focus.
5.4.3 Demographic variables
Comparing the differences in behavior between the tagging and simulated query activities
based on the three demographic variables reveals a stark contrast in results. In the tagging phase,
the only major differences in behavior were based on education; those with a degree were three
times as likely to note LITERAL OBJECTS than were non-degree holders, and non-degree holders
were far more likely to note ABSTRACT CONCEPTS and more than four times as likely to provide
VIEWER RESPONSES as part of their description of an editorial cartoon. While some differences in
tagging behavior were noted between genders and among political leanings, they were much
smaller differences than those based on education.
There are few surprises in this. Of the three variables, it was expected that education
would reveal the biggest differences simply because the other factors didn’t seem to be such as
would produce vastly different results. Nothing in any of the reviewed literature indicated that
any of these variables would make a big difference, but education, on its face, seemed most
likely to produce different results. It also seems that the differences found in education would
scale with a larger population, that what is shown is not a product of the sample size but of a
genuine dissimilarity in how the educated tag editorial cartoons differently than the not-yet
educated.
All of which made the demographic differences in behavior for the simulated query
activity the more surprising. In this phase, holding a degree or not made little difference in how
the cartoon would be searched for, but gender and political leaning seemed to make noticeable
differences. Women would search for editorial cartoons more often by ABSTRACT CONCEPT and
PEOPLE-RELATED ATTRIBUTES, while men would search based more on CONTENT/STORY and
PEOPLE. Liberals seemed far more likely to concentrate on LITERAL OBJECTS within the image as
part of their search, while both Moderates and Conservatives were more likely to use ABSTRACT
CONCEPTS, and Liberals were more likely to use PEOPLE as part of their search, but
Conservatives seemed more interested in PEOPLE-RELATED ATTRIBUTES. Granted, sample sizes
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were very small for each of these divisions, but it does point out potential demographics
questions for future research.
This is the mirror opposite of what was found in the tagging activity: education made
little difference here, while it made all the difference there. How can this be? How can holding a
degree or not make a big difference in how one describes editorial cartoons, but little difference
in how one searches for them? Why does gender make a difference in how cartoons are searched
for? And why would one’s political leaning make any difference at all? None of the literature
reviewed sheds any light on these questions, and this research was not designed to explain any of
these particular findings. But now that this exploratory research has found that these differences
may exist, future research might be able to better explain them, and thus move forward the
accurate and useful description of editorial cartoons for later retrieval.
Given these differences, given the mirror opposites found between the demographic
variables in the tagging and the query activities, how then did we arrive at substantially similar
overall use of frequencies in the 12 Classes? Why didn’t the differences in the demographic
variables produce differences in the overall results? As noted previously, Bates’
phenomenological comments seemed to be contradicted by the overall findings showing no
differences between the different research activities in this study, but something different seems
to be indicated here. There seem to be large differences in the experiences of the indexers based
not solely on whether they are tagging or querying, but also on gender, education, and political
leaning. It is only the combination of all of these that produced differences in this study,
seemingly calling into question to Bates’ assertion that it is activity alone that makes a
difference.
5.4.4 Effects of findings on practice
Possible effects of the finding of this research can be found in Section 5.3.3, which
centers on the development of edotiral cartoons systems and schema. This section focuses on the
confirmatory interviews and what the interviewees had to say about the applicability of the
results on a more practical basis. While those interviewed about the results of this study seemed
interested and willing to discuss the issues raised, the results of this study seemed to have little
affect on the professional cartoonists or the image professionals interviewed. While a priori
knowledge of what regular people would describe about an editorial cartoon cannot be
legitimately claimed, the predictions of these experts in the field concerning which of the Classes
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would be most often used closely mirrored the actual results. This was of some comfort to the
researcher, given the differences between the literature (which predicted that LITERAL OBJECTS
would be the most often used Class of descriptor and that ABSTRACT CONCEPTS would be of little
use) and the findings; to have some prediction of the results from professionals in the field, when
those results were so different from what was expected, provided some measure of validation,
some evidence that the results were not spurious but were in fact along the lines predicted by a
different authority than the literature.
The three cartoonists were unsurprised by the results of this research, especially after the
exact meanings of the various Classes were explained to them; they seemed to feel that their
work was the same as that of a columnist, and that in both cases the primary concern is the issue,
event, or thing being examined, with all other aspects of an editorial cartoon constituting a means
of getting that examination across to the reader. The four image professionals had less agreement
among them concerning the frequency of use among the 12 Classes. This is not to say that they
were surprised by the results of this research; instead, it is that, as a group, they predicted a
greater range of which Classes would be most often used. In addition, they also found slightly
more utility in the results, allowing that the findings here might better inform how they either
catalog images or how they should train their indexers. The supplementary evidence gathered as
a result of the interviews should provide a basis for future research, especially in the area of what
questions might be most profitably asked.
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APPENDIX A
INSTITUTIONAL REVIEW BOARD APPROVAL MEMORANDA
A.1 Initial Approval Memorandum
Office of the Vice President For Research
Human Subjects Committee
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
APPROVAL MEMORANDUM
Date: 9/12/2011
To: Christopher Landbeck
Address: 2100
Dept.: INFORMATION STUDIES
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research
Describing Editorial Cartoons: An Exploratory Study
The application that you submitted to this office in regard to the use of human subjects in the
proposal referenced above have been reviewed by the Secretary, the Chair, and one member of
the Human Subjects Committee. Your project is determined to be Expedited per per 45 CFR §
46.110(7) and has been approved by an expedited review process.
The Human Subjects Committee has not evaluated your proposal for scientific merit, except to
weigh the risk to the human participants and the aspects of the proposal related to potential risk
and benefit. This approval does not replace any departmental or other approvals, which may be
required.
If you submitted a proposed consent form with your application, the approved stamped consent
form is attached to this approval notice. Only the stamped version of the consent form may be
used in recruiting research subjects.
If the project has not been completed by 9/10/2012 you must request a renewal of approval for
continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your
expiration date; however, it is your responsibility as the Principal Investigator to timely request
renewal of your approval from the Committee.
You are advised that any change in protocol for this project must be reviewed and approved by
the Committee prior to implementation of the proposed change in the protocol. A protocol
change/amendment form is required to be submitted for approval by the Committee. In addition,
193
federal regulations require that the Principal Investigator promptly report, in writing any
unanticipated problems or adverse events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor is
reminded that he/she is responsible for being informed concerning research projects involving
human subjects in the department, and should review protocols as often as needed to insure that
the project is being conducted in compliance with our institution and with DHHS regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The
Assurance Number is FWA00000168/IRB number IRB00000446.
Cc: Corinne Jorgensen, Advisor
HSC No. 2011.6745
194
A.2 Approval of Amendament Memorandum
Office of the Vice President For Research
Human Subjects Committee
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
APPROVAL MEMORANDUM (for change in research protocol)
Date: 11/4/2011
To: Christopher Landbeck
Address: 2100
Dept.: INFORMATION STUDIES
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research (Approval for Change in Protocol)
Project entitled: Describing Editorial Cartoons: An Exploratory Study
The form that you submitted to this office in regard to the requested change/amendment to your
research protocol for the above-referenced project has been reviewed and approved.
If the project has not been completed by 9/10/2012, you must request a renewal of approval for
continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your
expiration date; however, it is your responsibility as the Principal Investigator to timely request
renewal of your approval from the Committee.
By copy of this memorandum, the chairman of your department and/or your major professor is
reminded that he/she is responsible for being informed concerning research projects involving
human subjects in the department, and should review protocols as often as needed to insure that
the project is being conducted in compliance with our institution and with DHHS regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The
Assurance Number is FWA00000168/IRB number IRB00000446.
Cc: Corinne Jorgensen, Advisor
HSC No. 2011.7366
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A.3 Re-Approval Memorandum
The Florida State University
Office of the Vice President For Research
Human Subjects Committee
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 9/25/2012
To: Christopher Landbeck
Address: 2100
Dept.: INFORMATION STUDIES
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research
Describing Editorial Cartoons: An Exploratory Study
Your request to continue the research project listed above involving human subjects has been
approved by the Human Subjects Committee. If your project has not been completed by
9/24/2013, you must request a renewal of approval for continuation of the project. As a courtesy,
a renewal notice will be sent to you prior to your expiration date; however, it is your
responsibility as the Principal Investigator to timely request renewal of your approval from the
committee.
If you submitted a proposed consent form with your renewal request, the approved stamped
consent form is attached to this re-approval notice. Only the stamped version of the consent
form may be used in recruiting of research subjects. You are reminded that any change in
protocol for this project must be reviewed and approved by the Committee prior to
implementation of the proposed change in the protocol. A protocol change/amendment form is
required to be submitted for approval by the Committee. In addition, federal regulations require
that the Principal Investigator promptly report in writing, any unanticipated problems or adverse
events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor are
reminded of their responsibility for being informed concerning research projects involving
human subjects in their department. They are advised to review the protocols as often as
necessary to insure that the project is being conducted in compliance with our institution and
with DHHS regulations.
Cc: Corinne Jorgensen, Advisor
HSC No. 2012.8992
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APPENDIX B
IMAGES USED IN THE PILOT STUDY
Figure 35 Pilot study image rami0 (Ramirez, 2011a)
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Figure 36 Pilot study image ande0 (Anderson, 2011a)
Figure 37 Pilot study image bree0 (Breen, 2001a)
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Figure 38 Pilot study image hand0 (Handleman, 2011a)
Figure 39 Pilot study image luck0 (Luckovich, 2011a)
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APPENDIX C
JÖRGENSEN’S 12 CLASSES
OBJECTS (OBJ)
This class contains objects which are classified as being literal (visually perceived)
objects and are named items which are perceived in the image.
In some cases, these could also be considered “interpretive,” as when participants express
uncertainty or conjecture as to exactly what an object is.
Attributes include: Text, Objects, Clothing, and Body Parts
Text (tx): Mention of specific test in the picture, such as the artist's name or other words
present (“Corot,” “Phillies”).
Object (ob): Mention of a specific object or category of objects. Includes living things
such as animals or plants (:table,” “dog”). Does not include People, Body Parts, or Clothing.
Clothing (cl): Specific items of clothing mentioned (“shirt,” “dress”). Includes
accessories (“tie,” “jewelry”). May also include animal “gear” (“harness,” “saddle”).
Body Part (bp): Any part of human or animal anatomy either specific (“head,” “hand,”
“knee”) or more general (“torso”). Includes hairstyle (“beard,” “bun”).
In some cases, these could also be considered “interpretive,” as when participants express
uncertainty or conjecture as to exactly what an object is. Attributes include: Text, Objects,
Clothing, and Body Parts.
PEOPLE (PEO)
The presence of a human form (People) was remarked upon with very high consistency.
The only attribute is People.
People (pe): Any mention of a human, singular or plural, of any age or sex. Refers to
specific persons depicted in the picture and includes pronoun references (“he,” “she,” “they,”
“his,” “her”). Code as Level Two if referring to specific person (s) not depicted in the picture.
PEOPLE-RELATED ATTRIBUTES (PRA)
These were often conjecture or declarations about such interpretive qualities as the nature
of the relationship among the people depicted in an image, their emotional state, or their
occupation or class membership.
Attributes include: Social Status, Relationship, Emotion
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Social Status (ss): Status of humans specifically commented upon, in addition to or in
place of terms coded People; includes occupation, race, nationality (“upper class,” “Japanese,”
“cab driver”).
Relationship (rl): Describes relationship experienced by humans in the picture or which
seems to be portrayed (“intimate,” “mother and child”).
Emotion (em): refers to specific mental states or mental activity or states of being
experienced or seeming to be experienced by the humans or animals in the picture (“sad,”
“confused,” “concentrating,” “afraid”).
ART HISTORICAL INFO (AHI)
This class includes information which is related to the production context of the
representation.
Attributes include: Type, Time Reference, Technique, Style, Representation, Medium,
Format, and Artist.
Type (ty): type of representation (“portrait,” “landscape,” “nude”).
Time Reference (tr): a reference to an era or time period in which the picture takes place
(“early 20's), or description of picture or style of picture as “old” etc.
Technique (tc): mention of artistic technique such as brushwork.
Style (sy): specific or general type of style mentioned (“Impressionism,” abstract,”
“naturalistic,” cartoony,” “loose,” etc.). A noun form such as Cartoon is coded Format.
Representation (rp): type of representation, such as photograph, painting, drawing,
illustration, etc.
Medium (md): materials in which item is executed (“oils,” “pencil”).
Format (fo): audience aimed at or specific publication type produced for (“children's art,”
“textbook,” “movie,” “cartoon,” “poster”).
Artist (ar): Naming of a specific artist (“Picasso,” “Hockney”).
COLOR (COL)
Includes specific, named colors and terms relating to various aspects of Color or Color
Value.
Color (co): Mention of a specific color (“red,” “blue,” “reddish-orange”).
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Color Value (cv): description of a group of colors related by a similar color value, or
other color qualities such as hue, tint. Includes general items such as “warm” or “dark” or
“light”.
VISUAL ELEMENTS (VIS)
Includes percepts such as Orientation, Shape, Visual Component (line, detail, lighting),
and Texture.
Other attributes include: Perspective, Motion, Focal Point, and Composition.
Perspective (ps): comments on quality or type of perspective. Also includes point-of-view
and scale (“very flat,” “no depth,” “top-down view”).
Motion (mt): motion or perceived motion of inanimate objects or depiction of motion
(“swoops,” “rushing,” “splashes”) or mention of sensation of motion as a result of some artistic
device. Depiction of intention human motion is coded Activity.
Focal Point (fp): area upon which attention is focused (“the man with the straw hat”) by
the use of another visual element, such as Composition (all heads are turned toward) or Color.
The device itself is coded separately.
Composition (cm): mention of method by which perceptual attention is focused on one
area, or general compositional or spatial relationships (“warm-colored object comes to
foreground”).
Orientation (or): direction of visual element (“diagonal,” “left to right,” “vertical”).
Shape (sh): specific shapes mentioned (“round,” “triangular,” “flat”).
Texture (te): mention of textural quality of depicted object or of picture (“shiny,”
“quilted,” “metallic”).
Visual component (vc): mentions of types of visual components or qualities such as line,
lighting, contrast, or other qualities (“stripes,” “reflection,” “shadow”).
LOCATION (LOC)
Includes attributes relating to both General and Specific locations within the picture.
Location - general (lg): a generalized location within the two-dimensional framework of
the image indicated by such terms as “foreground” or “background”. Can also refer to a general
section of the picture (“sky”).
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Location - specific (ls): locations of objects or specific picture elements specified by
prepositions (“on,” “under,” “above,” “around”) or specific locational words (“left,” “right,”
“center”).
DESCRIPTION (DES)
Includes descriptive adjectives and words referring to size or quantity.
Attributes include Number and Description.
Number (nu): number, quantity, size (“three,” “lots,” “large”).
Description (de): Adjectives referring to objects or people depicted. Include materials of
which objects are composed (“elderly,” “wooden”).
ABSTRACT CONCEPTS (ABC)
Includes attributes such as Abstract, Theme, and Symbolic Aspect, attributes that are
somehow stimulated by the image but are not necessarily tied to that specific image. Other
attributes include State and Atmosphere.
Abstract (ab): abstract terms used to describe the image as a whole (“unique,” “strange,”
“exotic,” “interesting”) which express concepts not easily depicted. If these types of terms are
used to describe objects in the image then they are classified as Description. If the term refers to
an affective response (“It makes me feel strange”) then is classified as Atmosphere. A subject or
topic of a picture is classified as Theme.
Theme (th): subject or topic of a picture (“transportation,” “exploration”). Also specific
discipline of study mentioned (“psychology,” “religion”).
Symbolic Aspect (sm): Statement that visual aspect is symbolic of specific meaning
(“man is so precise”). If noted only that the item is symbolic, use Level Two.
State (st): Condition of picture component or function fulfilled by component (“full,” “to
support”). For condition of object used as a adjective, use Description (“torn,” “rusty”). For
human mental or emotional states, use Emotion.
Atmosphere (at): Refers to general mood or atmosphere portrayed but not necessarily
seeming to be personally experienced by the human in the picture, but which may be experienced
by the viewer (“dreamlike” “funny,” “warm,” “sad”).
CONTENT/STORY (C/S)
Includes the attributes Activity, Event, Category, Time Aspect, and Setting relating to a
specific instance being depicted in the image.
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Activity (ac): Any physical action in which a human or animal participates, either
individually or as a group (“sitting,” “running”). Includes positional information about the body
(“slouched”) or conversational actions (“arguing”).
Event (ev): Group activity, performance, social gathering (picnic, circus, protest).
Category (ca): A noun referencing a type or genre of literature in relation to the image
(“romance,” “adventure,” “fairy tale,” “myth,” “science fiction”). Use of this type of terms as an
adjective (“a romantic picture”) is coded not as Category but as Atmosphere.
Setting (se): General external setting of a scene or activity (“restaurant,” “outdoors”).
Time Aspect (ta): duration of activity or reference to time component (“during,”
“while”). Also, time of day, seasons (“sunset,” “summer”).
EXTERNAL RELATION (EXT)
Includes attributes pertaining to relationships among attributes within an image or a
relationship with an external entity.
Attributes include Similarity, Reference, and Comparison.
Similarity (si): The statement that two images or objects look alike. Also, comparison
between two objects which are similar in some way (factory-like). May be used in conjunction
with elements within a single image or across images.
Reference (rf): reference to literary/entertainment etc. figure by way of comparison
(“John Boy Walton,” “Dracula”). Also references to external proper noun objects, institutions,
and locations (“Coca-Cola,” “Jersey Shore”).
Comparison (cp): Comparing current image as being different to ones viewed previously
or simultaneously. For comparisons involving similarities rather than differences, use Similarity.
VIEWER RESPONSE (VRS)
Expresses personal reaction to the image, such as Uncertainty, Conjecture and Personal
Reaction.
Uncertainty (un): expressions of uncertainty or confusion (“I just don't know,” “I can't
decide,” “Let's wait on that”).
Conjecture (cn): A phrase qualified by question marks or choices (“singles and dates?”),
can be combined with codes such as Location, Time Reference, activity, or Date.
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Personal Reaction (pr): statement of personal reaction to the picture which doesn't
mention visual elements: (“I think about the environment,” “yukky”). May be double-coded with
Abstract or other codes.
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APPENDIX D
COMMUNICATION AND CONSENT FOR TAGGING AND
QUERY TASKS FOR THE PILOT STUDY
This appendix contains all of the documentation that will be used to recruit participants to
the tagging and simulated query portions of the research.
D.1 Email to Department Heads of Potential Participants
Sir or Ma’am,
I am Chris Landbeck, a doctoral student in the School of Library and Information Studies
at Florida State University. I’m writing to you to obtain your permission to recruit participants
from your faculty for my dissertation research on the indexing of editorial cartoons. I am doing
this for two reasons: first, there may be reasons internal to your faculty that would make much
participation unwarranted; second, I would like to be sure that such an activity would be
welcomed among your faculty.
Participation involves two phases of testing: a tagging phase, where they will be asked to
provide key phrases and words that describe five editorial cartoons on two separate occasions
(for a total of ten cartoons); and (six weeks later) a simulated query phase, where they will be
asked to write a query for each of the same 10 cartoons. Participation is online, and can be done
at their convenience, although they will be encouraged to describe the cartoons as soon as each
set comes out. The full informed consent form can be found at:
http://clandbeck.cci.fsu.edu/steve/steve.php?task=loginController_loginPage
I propose to, with your permission, copy all of the email addresses for your faculty from
your webpage, and send them an email explaining who I am, what I’m asking them to do, and
why; the text of that mail can be found in the attached document. I anticipate having enough
participants to begin the study in mid-October. If you feel that we need to speak over the phone
or face-to-face, I am available at your convenience.
May I recruit participants for my research from your faculty?
Thanks very much for your consideration,
Chris Landbeck
Doctoral Candidate
School of Library and Information Studies
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Florida State University
D.2 Email to Recruit Participants
Dear Sir or Ma’am,
I am Chris Landbeck, a doctoral student in the School of Library and Information Studies at
Florida State University. Your department chair has given me permission to contact you about
participating in my research, which concerns the indexing of editorial cartoons for retrieval from large
databases and, through this, inclusion in the historical record. As little has been done in this area, I am
seeking your input in my research.
I have obtained permission to post the works of five recent Pulitzer Prize-winning editorial
cartoonists on a website that is hosted by the School of Library and Information Studies at Florida State
University. This site can be found here:
http://clandbeck.cci.fsu.edu/steve/steve.php?task=loginController_loginPage
This page, available now, provides a full, IRB-approved description of what will be asked of you
over the course of the study. On this site, on a Monday for two weeks running, I will post the most recent
works of these cartoonists, and will ask your to provide key phrases or words that describe those ten
cartoons; in all, this might take ten minutes each a week. Then, six weeks later, I will ask you to write a
search-engine type query on another website we host (you will not be asked to actually execute the
search, just to provide what that search would be). This will apply to all 10 of the cartoons already used,
and might take an hour of your time.
This invitation is being sent to the Departments of History, Political Science, and Art History and
the School of Library and Information Studies and Florida State University and the Department of
Journalism and Graphic Communication at Florida A&M University. I am doing this work to help resolve
a moral and ethical injustice, namely the exclusion of editorial cartoons from the record; why can we go
to the local newspaper, for instance, and discover the winner of the quilting bee 50 years ago at the First
Baptist Church but we cannot ever discover the subject and content of that paper’s editorial cartoons? It is
my hope that this research will be a first step toward finding a solution to this problem.
If you have any questions for me, I can be reached through the email address that sent this email,
and contact information for the FSU Human Subjects Board can be found at the link provided above.
Thanks in advance,
Chris Landbeck
Doctoral Candidate
School of Library and Information Studies
Florida State University
207
D.3 Consent for Tagging and for Query Activities
I'm Chris Landbeck, a PhD candidate in the School of Library and Information Studies at
Florida State University. This study is part of the research for my dissertation, which deals with
describing political cartoons. Specifically, I'm trying to find out how people would describe such
cartoons when given no guidance, template, or any other direction on how to do so. And this is
where you come in.
I am asking you to preform two different tasks for me in this research. First, I’ll ask you
to use a website that I’ve set up to tag editorial cartoons, which means to list some key phrases
and words that describe the cartoons in question. This would happen once a week, on a Monday,
for two weeks running, and would take about 15 minutes each time. Second, on another website
about six weeks later, I will ask you create a simulated search (like you’d use in a search engine)
for all ten of the cartoons you saw before. This task should take about 30 minutes. More specific
directions for each task is listed when those tasks begin.
All of the information you provide will be kept confidential to the extent allowed by law.
Both of the websites used in this study are hosted on secure servers at the School of Library and
Information Studies, Florida State University. Both are password protected, and the passwords
are only known by the researcher and the server administrator. This information will be kept on
file until September 15, 2013, at which time it will be electronically erased.
As this study deals with political cartoons, there are some inherent risks and rewards for
your participation. The risks center around the subject matter of the cartoons: you may find the
opinions of the cartoonists to be objectionable, or you may find he images to be extraordinarily
funny. The benefits of participating are minimal, and center on the potential pleasure found in
each cartoon. In both cases, your participation is voluntary, and you may stop at any time.
If you have any questions concerning this research study, please contact me at (850) 644-
8117 or [email protected], or you may contact the faculty supervisor of this study, Dr. Corinne
Jörgensen at (850) 644-5775 or at [email protected].
If you have any questions about your rights as a subject/participant in this research, or if
you feel you have been placed at risk, you may contact the Chair of the Human Subjects
Committee, Institutional Review Board, through the Vice President for the Office of Research at
(850) 644-8633.
208
Registration for this website requires an email address and password, the latter of which
you will choose for yourself. Registering in this way constitutes your consent to participate in
this research and for the data you provide to be used in this research.
Thanks very much for your help in this research.
209
D.4 Screenshots, Tagging Website
Figure 40 Screen 1a – welcome page (top)
210
Figure 41 Screen 1b – welcome page (bottom)
211
Figure 42 Screen 2 – Registration page
Figure 43 Screen 3 – Thank You and Instructions page
212
Figure 44 Screen 4 – Tagging start page
213
Figure 45 Screen 5 – Example of Blank Tagging page
214
Figure 46 Screen 6 – Example of Filled-In Tagging page
Figure 47 Screen 7 – Done and Thank You page (Week 1)
215
Figure 48 Screen 8 – Done and Reminder page (Week 2)
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APPENDIX E
COMMUNICATION, CONSENT, AND SCRIPT FOR
INTERVIEWS FOR THE PILOT TEST
E.1 Email to Potential Interviewees
Sir/Ma’am,
I am Chris Landbeck, a doctoral candidate at Florida State University. My research looks
into how editorial cartoons are both described and searched for, and I am looking for telephone
interviewees to assess the usefulness of the findings of this research, and whether these findings
mirror what is known in the field. A perusal of the literature shows that you may be someone that
could help shed light on this.
I have gathered information about the description of editorial cartoons and about queries
for such images. The data generated in these activities was analyzed using Jörgensen’s 12
Classes of image description to see how editorial cartoons compared to other kinds of images in
the terms used to describe them. As part of this interview, you will be given these 12 Classes
beforehand and asked to rank them in terms of importance for describing editorial cartoons.
During the interview, your predictions will be compared to the actual results, and we will discuss
these – and anything else you deem important to the conversation – until we are satisfied that
we’ve covered everything.
This research project has been approved by and has the full support of Florida State
University.
The interview itself will be conducted as follows: after initially contacting you, sending
you the 12 Classes, and setting up a time for the interview, I will call you via a recording service
called recordmycalls.com, which will allow the interview to be recorded via the Web. At that
time I will introduce myself, make sure I am talking to the right person, ask for your consent to
record this interview, and will then read the entirety of this document to you. After this I will ask
for your consent to be interviewed, and after it is secured the interview will begin. It is estimated
that the interview will take 20-30 minutes, and will center on whether the use of the 12 Classes is
appropriate for editorial carton, and whether the findings of the research matter to you.
Your participation is voluntary, and you are free to decline. If you choose not to
participate or to withdraw from the study at any time, there will be no penalty. The results of the
217
research study may be published, but your name will not be used. The research report will be
made available to any participant who would like to see it.
Confidentiality will be maintained to the extent allowed by law. Identifying information
will be maintained by the researchers in a locked file. Digital recordings will be stored by the
researchers on a password protected laptop. All paper and electronic files related to this research
project will be destroyed no later than two years from the date of this project (September 15,
2013).
There are no foreseeable risks or discomforts related to your participation and the results
of the research promise to library and information studies, history and political science, art
history, and the cartooning profession.
Please note that if at any time you have any questions about your rights as a
subject/participant in this research, or if you feel you have been placed at risk, you can contact
the Chair of the Human Subjects Committee, Institutional Review Board, through the Vice
President for the Office of Research at (850) 644-8633.
If at any time you have any questions about this research or your participation in it,
please contact:
Chris Landbeck
School of Library & Information Studies
Florida State University
A copy of this consent agreement will be sent to you at your request, and a recording of
the interview will be made available to you on the same basis.
218
E.2 Pre-Interview Email (with Jörgensen’s 12 Classes) Hello, and thanks for helping me with my research.
This is a list of 12 classes of image description, based on work that has come out of the
fields of library and information science. I have gathered data from a number of participants and
have slotted their comments into these 12 classes, and a few more that cropped up along the way.
I will be interviewing you by phone about these classes and the frequency of their use. I
would like to compare the order that you, in your professional capacity, would put them in to that
resulting from the research. After that, I would like to have a simple conversation about the
things that I, as a researcher, need to know about the creation of or access to such images, again
based on what you know as a professional.
Please place these items in order from most important to least important for editorial
cartoons:
Abstract Concepts
Art Historical Information
Color
Content/Story
Description
External Relation
Literal Object
Location
People
People Qualities
Personal Reaction
Visual Elements
I look forward to talking with you soon,
Chris Landbeck
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E.3 Informed Consent and Script for Semi-Structured Interview
[Participants in this phase of the study will already have Jörgensen’s 12 Classes and will
have had the opportunity to put those classes in the order that they think is most important.]
“Good morning/afternoon, Mr./Ms.____________________. Thanks very much for
taking the time out of your day for this interview. Are you ready?
[If yes, continue. If no, arrange another interview time.]
“May I record this interview?”
[If yes, continue. If no, call back using regular phone service.]
“This next part I have to read to you because of University rules. Ready?”
“Thanks very much for helping me with my research.
“I am Chris Landbeck, a doctoral candidate at Florida State University, and I’m
conducting telephone interviews to assess the usefulness of the findings of my research into
editorial cartoons, and whether these findings mirror what is known in the field. It is as an
interviewee that your help is being sought.
“The previous two parts of this three-part study gathered information about the
description of editorial cartoons and about queries for such images. The data generated in these
activities was analyzed using Jörgensen’s 12 Classes of image description to see how editorial
cartoons compared to other kinds of images in the terms used to describe them. As part of this
interview, you have been given these 12 Classes and asked to rank them in terms of importance
for describing editorial cartoons. During the interview, your predictions will be compared to the
actual results, and we will discuss these – and anything else you deem important to the
conversation – until we are satisfied that we’ve covered everything.
“This research project has been approved by and has the full support of Florida State
University.
“The interview itself will be conducted as follows: having already contacted you to
arrange this interview and sending you the 12 Classes, I have called you via a recording service
called recordmycalls.com, which allows the interview to be recorded via the Web. After reading
the require informed consent document to you, I will ask for your consent to be interviewed, and
after it is secured the interview will begin. It is estimated that the interview will take 20-30
minutes, and will center on whether the use of the 12 Classes is appropriate for editorial carton,
and whether the findings of the research matter to you.
220
“Your participation is voluntary, and you are free to decline. If you choose not to
participate or to withdraw from the study at any time, there will be no penalty. The results of the
research study may be published, but your name will not be used. The research report will be
made available to any participant who would like to see it.
“Confidentiality will be maintained to the extent allowed by law. Identifying information
will be maintained by the researchers in a locked file. Digital recordings will be stored by the
researchers on a password protected laptop. All paper and electronic files related to this research
project will be destroyed no later than two years from the date of this project (September 15,
2013).
“There are no foreseeable risks or discomforts related to your participation and the results
of the research promise to library and information studies, history and political science, art
history, and the cartooning profession.
“Please note that if at any time you have any questions about your rights as a
subject/participant in this research, or if you feel you have been placed at risk, you can contact
the Chair of the Human Subjects Committee, Institutional Review Board, through the Vice
President for the Office of Research at (850) 644-8633.
“If at any time you have any questions about this research or your participation in it,
please contact: Chris Landbeck, School of Library & Information Studies, Florida State
University at [email protected].
“Do I have your consent to proceed with this interview as outlined?”
[If yes, continue. If no, thanks the person for their time, and end the discussion.]
“Have you had a chance to put those classes of image description in order?”
[If no, allow some time for the order to be made right then.]
[Assuming an affirmative response…]
“Wonderful! What order do you have them in, please?”
[Write down the interviewee’s order for later reference]
“Why this order? What prompted you to, for instance, put the first one first?”
[Await reply]
“And why are the ones at the bottom less important?
[Await reply]
221
“Mr./Ms.____________________, I have here the order of those classes as discovered in
my research.”
[List classes]
“Does this surprise you much? Why?”
[Await response]
“Do you think that any of this might change the way you do your own work? Why?”
[Await answer]
From here, the interview will be allowed to cover whatever topics or aspects of the
research that is deemed desirable by both the researcher and the interviewee.
“Thanks very much for speaking with me today. One last thing, is there anyone else you
can think of that might want to participate in my research as you have today?”
[If yes, get contact information.]
“Would you like to see the results of his research?”
[Make note of answer.]
“OK, thanks again for your time.”
End interview.
222
E.4 Screenshots, Query website
Figure 49 Screen 1 – Welcome page
Figure 50 Screen 2 – Query Starting page
223
Figure 51 Screen 3 – Example of Blank Query page
224
Figure 52 Screen 4 – Example of Filled-In Query page
Figure 53 Screen 5 – Thank You page
225
APPENDIX F
PILOT STUDY RECRUITING DOCUMENTATION
Fellow doctoral students,
I am asking for your help in pilot testing the survey instrument for my dissertation on
editorial cartoons. This involves two phases of testing: a tagging phase, where you will be asked
to provide key phrases and words that describe five editorial cartoons; and (few days later) a
simulated query phase, where you will be asked to write a query for each of the same five
cartoons. Participation is online, and can be done at your convenience.
http://clandbeck.cci.fsu.edu/steve/steve.php?task=loginController_loginPage
This link leads to the informed consent page of the website, and is available before
logging in. A pilot testers, the gap between the phases will be about a week, not the six weeks
that will be used in the actual study, and the pilot study data will only be retained for a period of
three months after the last participant’s data is collected.
Any feedback, on any portion of the instrument, is welcomed, and your help in finalizing
this will be most appreciated.
Thanks in advance,
Chris Landbeck
Doctoral Student
SLIS, Florida State University
226
APPENDIX G
IMAGES USED IN THE FULL STUDY, BY WEEK
G.1: Week 1 (Monday, October 31, 2011)
Figure 54 ande1 [in color] (Anderson, 2011b)
227
Figure 55 bree1 [in color] (Breen, 2001b)
Figure 56 hand1 [in color] (Handleman, 2011b)
228
Figure 57 luck1 [in color] (Luckovich, 2011b)
Figure 58 rame1 [in color] (Ramirez, 2011b)
229
G.2: Week 2 (Monday, November 7, 2011)
Figure 59 ande2 [in color] (Anderson, 2011c)
Figure 60 bree2 (in black & white) (Breen, 2011c)
230
Figure 61 hand2 [in color] (Handleman, 2011c)
Figure 62 luck2 [in color] (Luckovich, 2011c)
231
Figure 63 rame2 [in color] (Ramirez, 2011c)
232
APPENDIX H
SCREENSHOTS OF THE REVISED INTERFACES
H.1: Tagging activity
Figure 64 Tagging phase screenshot -- Welcome page (top)
233
Figure 65 Tagging phase screenshot -- Welcome page (bottom)
234
Figure 66 Tagging phase screenshot -- registration page
Figure 67 Tagging phase screenshot -- instruction page
235
Figure 68 Tagging phase screenshot -- staging area page
236
Figure 69 Tagging phase screenshot -- blank tagging page
237
Figure 70 Tagging phase screenshot -- filled-in tagging page with editing options
238
Figure 71 Tagging phase screenshot -- thank you page, Week 1
Figure 72 Tagging phase screenshot -- thank you page, Week 2
239
H.2: Simulated query activity
Figure 73 Query phase screenshot -- welcome page
240
Figure 74 Query phase screenshot -- staging area page
241
Figure 75 Query phase screenshot -- blank query page (top)
242
Figure 76 Query phase screenshot -- blank query page (bottom)
243
Figure 77 Query phase screenshot -- filled-in query page with editing options
244
Figure 78 Query phase screenshot -- thank you page
245
APPENDIX I
RAW TAGGING ACTIVITY DATA
This data is in nine-point font to accommodate the size of the table, which in turn promotes the readability of the data. It was
felt that keeping the data for each tag was more important than the strict interpretation of APA formatting rules.
Table 33
Data from tagging activity PK term attrib Class p_id edu_type gen politics
10001 (01ande1:001) 201[2] election (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10002 (01ande1:002) about current events (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
10003 (01ande1:002) about current events (th) (th) ABC 12 nonAdvDgreHldr F Moderate
10004 (01ande1:003) Anger again[st] Obama (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
10005 (01ande1:003) Anger again[st] Obama (th) (th) ABC 29 nonAdvDgreHldr F Conservative
10006 (01ande1:004) antiamerican (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10007 (01ande1:005) anti-obama (at) (at) ABC 17 AdvDgreHldr F Moderate
10008 (01ande1:006) anti-republican (at) (at) ABC 17 AdvDgreHldr F Moderate
10009 (01ande1:007) autumn (ta) (ta) C_S 4 AdvDgreHldr M Liberal
10010 (01ande1:008) Barack Obama (PEO) (PEO) PEO 4 AdvDgreHldr M Liberal
10011 (01ande1:009) burn (WTF) (WTF) WTF 9 AdvDgreHldr F Liberal
10012 (01ande1:010) control (ab) (ab) ABC 42 nonAdvDgreHldr M Moderate
10013 (01ande1:011) corrupt (ab) (ab) ABC 42 nonAdvDgreHldr M Moderate
10014 (01ande1:012) crash (se) (se) C_S 9 AdvDgreHldr F Liberal
10015 (01ande1:013) Critical of Republicans (pr) (pr) VRE 16 AdvDgreHldr M Moderate
10016 (01ande1:013) Critical of Republicans (th) (th) ABC 16 AdvDgreHldr M Moderate
10017 (01ande1:014) Democrats (ss) (ss) PRA 9 AdvDgreHldr F Liberal
10018 (01ande1:015) Democrats (ss) (ss) PRA 35 AdvDgreHldr F Moderate
10019 (01ande1:016) democrats (ss) (ss) PRA 43 AdvDgreHldr F Liberal
10020 (01ande1:017) Democrats (ss) (ss) PRA 22 nonAdvDgreHldr F Moderate
10021 (01ande1:018) democrats (ss) (ss) PRA 24 nonAdvDgreHldr F Moderate
10022 (01ande1:019) Disapproval (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10023 (01ande1:020) Economic slump (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10024 (01ande1:021) economy crash (th) (th) ABC 19 AdvDgreHldr F Moderate
10025 (01ande1:022) election (th) (th) ABC 4 AdvDgreHldr M Liberal
10026 (01ande1:023) fail (ab) (ab) ABC 9 AdvDgreHldr F Liberal
246
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10027 (01ande1:024) failed (ab) (ab) ABC 8 nonAdvDgreHldr F Moderate
10028 (01ande1:025) failure (ab) (ab) ABC 17 AdvDgreHldr F Moderate
10029 (01ande1:026) Failure (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10030 (01ande1:027) fall (ta) (ta) C_S 4 AdvDgreHldr M Liberal
10031 (01ande1:028) foreign (ab) (ab) ABC 25 nonAdvDgreHldr F Conservative
10032 (01ande1:029) foreign policy (th) (th) ABC 9 AdvDgreHldr F Liberal
10033 (01ande1:029) foreign policy (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10034 (01ande1:030) foreign policy (th) (th) ABC 16 AdvDgreHldr M Moderate
10035 (01ande1:030) foreign policy (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10036 (01ande1:031) Foreign Policy (th) (th) ABC 18 AdvDgreHldr M Liberal
10037 (01ande1:031) Foreign Policy (tx) (tx) LOB 18 AdvDgreHldr M Liberal
10038 (01ande1:032) Foreign Policy (th) (th) ABC 4 AdvDgreHldr M Liberal
10039 (01ande1:032) Foreign Policy (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10040 (01ande1:033) foreign policy (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10041 (01ande1:033) foreign policy (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10042 (01ande1:034) foreign policy (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10043 (01ande1:034) foreign policy (tx) (tx) LOB 3 nonAdvDgreHldr F Conservative
10044 (01ande1:035) foreign policy success (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10045 (01ande1:036) Foreign policy-Obama (th) (th) ABC 35 AdvDgreHldr F Moderate
10046 (01ande1:037) Foriegn policy (th) (th) ABC 37 nonAdvDgreHldr M Moderate
10047 (01ande1:038) funny (at) (at) ABC 5 nonAdvDgreHldr M Conservative
10048 (01ande1:039) GOP (ss) (ss) PRA 9 AdvDgreHldr F Liberal
10052 (01ande1:039) GOP (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10049 (01ande1:040) GOP (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10053 (01ande1:040) GOP (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10050 (01ande1:041) GOP (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
10054 (01ande1:041) GOP (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10051 (01ande1:042) GOP (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
10055 (01ande1:042) GOP (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10056 (01ande1:043) GOP fail (pr) (pr) VRE 1 nonAdvDgreHldr F Moderate
10057 (01ande1:043) GOP fail (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10058 (01ande1:044) Houston Chronicle (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
10059 (01ande1:044) Houston Chronicle (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10060 (01ande1:045) i have no idea (un) (un) VRE 12 nonAdvDgreHldr F Moderate
10061 (01ande1:046) i need to read more (un) (un) VRE 12 nonAdvDgreHldr F Moderate
247
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10062 (01ande1:047) iraq war (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10063 (01ande1:048) ironic (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10064 (01ande1:049) ironic (at) (at) ABC 24 nonAdvDgreHldr F Moderate
10065 (01ande1:050) ironic (at) (at) ABC 6 nonAdvDgreHldr M Conservative
10066 (01ande1:051) irony (at) (at) ABC 6 nonAdvDgreHldr M Conservative
10067 (01ande1:052) Liberal (at) (at) ABC 43 AdvDgreHldr F Liberal
10068 (01ande1:053) liberal (at) (at) ABC 24 nonAdvDgreHldr F Moderate
10069 (01ande1:054) liberal (at) (at) ABC 6 nonAdvDgreHldr M Conservative
10070 (01ande1:055) liberals gaining power (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10071 (01ande1:055) liberals gaining power (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10072 (01ande1:056) metaphorical (ab) (ab) ABC 4 AdvDgreHldr M Liberal
10073 (01ande1:057) Nick Anderson (ar) (ar) AHI 4 AdvDgreHldr M Liberal
10074 (01ande1:057) Nick Anderson (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10075 (01ande1:058) no contenders (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
10076 (01ande1:058) no contenders (th) (th) ABC 10 nonAdvDgreHldr M Moderate
10077 (01ande1:059) No fly zone (th) (th) ABC 4 AdvDgreHldr M Liberal
10078 (01ande1:059) No fly zone (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10079 (01ande1:060) no fly zone (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10080 (01ande1:060) no fly zone (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
10081 (01ande1:061) No Fly Zone (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10082 (01ande1:061) No Fly Zone (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10083 (01ande1:062) no fly zone (th) (th) ABC 9 AdvDgreHldr F Liberal
10084 (01ande1:062) no fly zone (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10085 (01ande1:063) no fly zone (th) (th) ABC 18 AdvDgreHldr M Liberal
10086 (01ande1:063) no fly zone (tx) (tx) LOB 18 AdvDgreHldr M Liberal
10087 (01ande1:064) no foreign policy (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10088 (01ande1:065) No one likes Obama anymore (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
10089 (01ande1:065) No one likes Obama anymore (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10090 (01ande1:066) not liked (at) (at) ABC 8 nonAdvDgreHldr F Moderate
10091 (01ande1:067) Obama (PEO) (PEO) PEO 4 AdvDgreHldr M Liberal
10092 (01ande1:067) Obama (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10093 (01ande1:068) Obama (PEO) (PEO) PEO 16 AdvDgreHldr M Moderate
10094 (01ande1:068) Obama (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10095 (01ande1:069) obama (PEO) (PEO) PEO 1 nonAdvDgreHldr F Moderate
10096 (01ande1:069) obama (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
248
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10097 (01ande1:070) Obama (PEO) (PEO) PEO 22 nonAdvDgreHldr F Moderate
10098 (01ande1:070) Obama (tx) (tx) LOB 22 nonAdvDgreHldr F Moderate
10099 (01ande1:071) Obama (PEO) (PEO) PEO 38 nonAdvDgreHldr F Liberal
10100 (01ande1:071) Obama (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10101 (01ande1:072) obama (PEO) (PEO) PEO 9 AdvDgreHldr F Liberal
10102 (01ande1:072) obama (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10103 (01ande1:073) obama (PEO) (PEO) PEO 18 AdvDgreHldr M Liberal
10104 (01ande1:073) obama (tx) (tx) LOB 18 AdvDgreHldr M Liberal
10105 (01ande1:074) obama (PEO) (PEO) PEO 25 nonAdvDgreHldr F Conservative
10106 (01ande1:074) obama (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10107 (01ande1:075) Obama (PEO) (PEO) PEO 43 AdvDgreHldr F Liberal
10108 (01ande1:075) Obama (tx) (tx) LOB 43 AdvDgreHldr F Liberal
10109 (01ande1:076) obama failing (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
10110 (01ande1:076) obama failing (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10111 (01ande1:077) Obama Weak (pr) (pr) VRE 20 nonAdvDgreHldr M Moderate
10112 (01ande1:077) Obama Weak (th) (th) ABC 20 nonAdvDgreHldr M Moderate
10113 (01ande1:078) plane (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10114 (01ande1:079) plane (ob) (ob) LOB 25 nonAdvDgreHldr F Conservative
10115 (01ande1:080) plane crash (se) (se) C_S 1 nonAdvDgreHldr F Moderate
10116 (01ande1:081) Plane crash (se) (se) C_S 39 nonAdvDgreHldr M Conservative
10117 (01ande1:082) plane crash (se) (se) C_S 26 nonAdvDgreHldr M Conservative
10118 (01ande1:083) pointed (at) (at) ABC 4 AdvDgreHldr M Liberal
10119 (01ande1:084) politics (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10120 (01ande1:085) presidential election (th) (th) ABC 4 AdvDgreHldr M Liberal
10121 (01ande1:086) Republicans (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10122 (01ande1:087) Republicans (ss) (ss) PRA 16 AdvDgreHldr M Moderate
10123 (01ande1:088) Republicans (ss) (ss) PRA 43 AdvDgreHldr F Liberal
10124 (01ande1:089) republicans (ss) (ss) PRA 9 AdvDgreHldr F Liberal
10125 (01ande1:090) republicans are weaker (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10126 (01ande1:090) republicans are weaker (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10127 (01ande1:091) republicans[] (ss) (ss) PRA 6 nonAdvDgreHldr M Conservative
10128 (01ande1:091a) [democrats] (ss) (ss) PRA 6 nonAdvDgreHldr M Conservative
10129 (01ande1:092) shot down (WTF) (WTF) WTF 18 AdvDgreHldr M Liberal
10130 (01ande1:093) shot down (WTF) (WTF) WTF 4 AdvDgreHldr M Liberal
10131 (01ande1:094) shot down (WTF) (WTF) WTF 40 nonAdvDgreHldr F Liberal
249
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10132 (01ande1:095) Strong Foreign Policy (th) (th) ABC 43 AdvDgreHldr F Liberal
10133 (01ande1:096) terrorism (th) (th) ABC 15 nonAdvDgreHldr F Liberal
10134 (01ande1:097) typical republicans (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10135 (01ande1:097) typical republicans (th) (th) ABC 36 nonAdvDgreHldr M Liberal
10136 (01ande1:098) U.S. foreign policy-Obama (th) (th) ABC 19 AdvDgreHldr F Moderate
10137 (01ande1:099) untrue (ab) (ab) ABC 40 nonAdvDgreHldr F Liberal
10138 (01ande1:100) weak (ab) (ab) ABC 4 AdvDgreHldr M Liberal
10139 (01ande1:100) weak (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10140 (01ande1:101) weak (ab) (ab) ABC 25 nonAdvDgreHldr F Conservative
10141 (01ande1:101) weak (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10142 (01ande1:102) weak 2012 GOP (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
10143 (01ande1:102) weak 2012 GOP (th) (th) ABC 10 nonAdvDgreHldr M Moderate
10144 (01ande1:103) TRUE (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10145 (01ande1:104) Censorship (th) (th) ABC 23 nonAdvDgreHldr F Liberal
10146 (01ande1:105) Controversial (at) (at) ABC 23 nonAdvDgreHldr F Liberal
10147 (01ande1:106) democrats overstepping power (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
10148 (01ande1:106) democrats overstepping power (th) (th) ABC 31 nonAdvDgreHldr F Conservative
10149 (01ande1:107) Foriegn Policy (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10150 (01ande1:108) GOP crashing (ac) (ac) C_S 21 nonAdvDgreHldr F Liberal
10151 (01ande1:109) GOP vs. Obama (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10152 (01ande1:109) GOP vs. Obama (th) (th) ABC 21 nonAdvDgreHldr F Liberal
10153 (01ande1:110) irony (at) (at) ABC 11 nonAdvDgreHldr M Liberal
10154 (01ande1:111) Joke (ca) (ca) C_S 27 nonAdvDgreHldr M Liberal
10155 (01ande1:112) low enforcement (WTF) (WTF) WTF 13 nonAdvDgreHldr F Moderate
10156 (01ande1:113) mudslinging (ab) (ab) ABC 11 nonAdvDgreHldr M Liberal
10157 (01ande1:113) mudslinging (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10158 (01ande1:114) no fly zone (th) (th) ABC 32 nonAdvDgreHldr F Conservative
10159 (01ande1:114) no fly zone (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10160 (01ande1:116) no real strong candidate (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10161 (01ande1:116) no real strong candidate (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10162 (01ande1:117) Obama (PEO) (PEO) PEO 30 nonAdvDgreHldr M Conservative
10163 (01ande1:117) Obama (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10164 (01ande1:118) Obama (PEO) (PEO) PEO 33 nonAdvDgreHldr F Moderate
10165 (01ande1:118) Obama (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10166 (01ande1:121) Obama criticism (ab) (ab) ABC 21 nonAdvDgreHldr F Liberal
250
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10167 (01ande1:121) Obama criticism (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10168 (01ande1:122) Obama Foreign Policy (th) (th) ABC 43 AdvDgreHldr F Liberal
10169 (01ande1:122) Obama Foreign Policy (tx) (tx) LOB 43 AdvDgreHldr F Liberal
10170 (01ande1:123) republican (ss) (ss) PRA 32 nonAdvDgreHldr F Conservative
10171 (01ande1:124) Republicans (ss) (ss) PRA 30 nonAdvDgreHldr M Conservative
10172 (01ande1:125) The GOP argument is weak (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
10173 (01ande1:126) unrealistic (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
10174 (01ande1:127) weakening support (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
10175 (01ande1:127) weakening support (th) (th) ABC 13 nonAdvDgreHldr F Moderate
10176 (02bree1:001) 99% (rf) (rf) ERE 43 AdvDgreHldr F Liberal
10177 (02bree1:002) 99% (rf) (rf) ERE 22 nonAdvDgreHldr F Moderate
10178 (02bree1:003) 99% (rf) (rf) ERE 10 nonAdvDgreHldr M Moderate
10179 (02bree1:004) 1 percent (rf) (rf) ERE 7 nonAdvDgreHldr M Moderate
10180 (02bree1:005) 99 percent (rf) (rf) ERE 7 nonAdvDgreHldr M Moderate
10181 (02bree1:006) about time (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10182 (02bree1:007) angry (at) (at) ABC 1 nonAdvDgreHldr F Moderate
10183 (02bree1:008) Animal abuse needs to stop (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
10184 (02bree1:008) Animal abuse needs to stop (th) (th) ABC 29 nonAdvDgreHldr F Conservative
10185 (02bree1:009) animal cruelty (th) (th) ABC 18 AdvDgreHldr M Liberal
10186 (02bree1:010) animal cruelty (th) (th) ABC 23 nonAdvDgreHldr F Liberal
10187 (02bree1:011) animal cruelty (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10188 (02bree1:012) animal freedom (th) (th) ABC 15 nonAdvDgreHldr F Liberal
10189 (02bree1:013) Animal rights (th) (th) ABC 9 AdvDgreHldr F Liberal
10190 (02bree1:014) Animal Rights (th) (th) ABC 16 AdvDgreHldr M Moderate
10191 (02bree1:015) Animal Rights (th) (th) ABC 18 AdvDgreHldr M Liberal
10192 (02bree1:016) animal rights (th) (th) ABC 17 AdvDgreHldr F Moderate
10193 (02bree1:017) animal rights (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10194 (02bree1:018) animals (ob) (ob) LOB 15 nonAdvDgreHldr F Liberal
10195 (02bree1:019) animals being compared to blac (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
10196 (02bree1:019) animals being compared to blac (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10197 (02bree1:020) annoying (at) (at) ABC 40 nonAdvDgreHldr F Liberal
10198 (02bree1:021) conservative (ss) (ss) PRA 26 nonAdvDgreHldr M Conservative
10199 (02bree1:022) dolphins (ob) (ob) LOB 9 AdvDgreHldr F Liberal
10200 (02bree1:023) dolphins (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10201 (02bree1:024) dont get it (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
251
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10202 (02bree1:025) Economical unrest (th) (th) ABC 43 AdvDgreHldr F Liberal
10203 (02bree1:026) far side (si) (si) ERE 26 nonAdvDgreHldr M Conservative
10204 (02bree1:027) Fight (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10205 (02bree1:028) fight (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
10206 (02bree1:029) Fight the power! (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10207 (02bree1:030) fish (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10208 (02bree1:031) free (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10209 (02bree1:032) Free Shamu (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10210 (02bree1:033) freedom (ab) (ab) ABC 9 AdvDgreHldr F Liberal
10211 (02bree1:034) funny (at) (at) ABC 34 nonAdvDgreHldr M Moderate
10212 (02bree1:035) going too far (at) (at) ABC 42 nonAdvDgreHldr M Moderate
10213 (02bree1:036) i like animals (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
10214 (02bree1:037) i love animals (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
10215 (02bree1:038) Inequality (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10216 (02bree1:039) Justice (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10217 (02bree1:040) misinformed (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
10218 (02bree1:041) not useful (pr) (pr) VRE 40 nonAdvDgreHldr F Liberal
10219 (02bree1:042) Occupy (th) (th) ABC 4 AdvDgreHldr M Liberal
10220 (02bree1:042) Occupy (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10221 (02bree1:043) occupy (th) (th) ABC 26 nonAdvDgreHldr M Conservative
10222 (02bree1:043) occupy (tx) (tx) LOB 26 nonAdvDgreHldr M Conservative
10223 (02bree1:044) Occupy (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10224 (02bree1:044) Occupy (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
10225 (02bree1:045) Occupy movement (th) (th) ABC 9 AdvDgreHldr F Liberal
10226 (02bree1:046) Occupy movement (th) (th) ABC 16 AdvDgreHldr M Moderate
10227 (02bree1:047) Occupy Movement (th) (th) ABC 35 AdvDgreHldr F Moderate
10228 (02bree1:048) occupy movement (th) (th) ABC 4 AdvDgreHldr M Liberal
10229 (02bree1:049) Occupy movement (th) (th) ABC 19 AdvDgreHldr F Moderate
10230 (02bree1:050) occupy movement is everywhere (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10231 (02bree1:050) occupy movement is everywhere (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10232 (02bree1:051) Occupy Wall Street (th) (th) ABC 43 AdvDgreHldr F Liberal
10233 (02bree1:052) Occupy Wall Street (th) (th) ABC 4 AdvDgreHldr M Liberal
10234 (02bree1:053) Occupy Wall Street (th) (th) ABC 23 nonAdvDgreHldr F Liberal
10235 (02bree1:054) Occupy Wallstreet (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10236 (02bree1:055) octopus (ob) (ob) LOB 9 AdvDgreHldr F Liberal
252
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10237 (02bree1:056) octopus (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10238 (02bree1:057) orcas (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10239 (02bree1:058) Orcas are slaves! (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10240 (02bree1:059) overkill (at) (at) ABC 12 nonAdvDgreHldr F Moderate
10241 (02bree1:060) parody (ca) (ca) C_S 16 AdvDgreHldr M Moderate
10242 (02bree1:061) Parody (ca) (ca) C_S 4 AdvDgreHldr M Liberal
10243 (02bree1:062) PETA (ss) (ss) PRA 9 AdvDgreHldr F Liberal
10244 (02bree1:062) PETA (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10245 (02bree1:063) PETA (ss) (ss) PRA 16 AdvDgreHldr M Moderate
10246 (02bree1:063) PETA (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10247 (02bree1:064) PETA (ss) (ss) PRA 35 AdvDgreHldr F Moderate
10248 (02bree1:064) PETA (tx) (tx) LOB 35 AdvDgreHldr F Moderate
10249 (02bree1:065) PETA (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10250 (02bree1:065) PETA (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10251 (02bree1:066) peta (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
10252 (02bree1:066) peta (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10253 (02bree1:067) peta (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
10254 (02bree1:067) peta (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10255 (02bree1:068) peta (ss) (ss) PRA 33 nonAdvDgreHldr F Moderate
10256 (02bree1:068) peta (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10257 (02bree1:069) PETA (ss) (ss) PRA 15 nonAdvDgreHldr F Liberal
10258 (02bree1:069) PETA (tx) (tx) LOB 15 nonAdvDgreHldr F Liberal
10259 (02bree1:070) PETA (ss) (ss) PRA 34 nonAdvDgreHldr M Moderate
10260 (02bree1:070) PETA (tx) (tx) LOB 34 nonAdvDgreHldr M Moderate
10261 (02bree1:071) peta is ridiculous (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
10262 (02bree1:071) peta is ridiculous (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10263 (02bree1:072) peta petition (WTF) (WTF) WTF 3 nonAdvDgreHldr F Conservative
10264 (02bree1:073) pointless (at) (at) ABC 30 nonAdvDgreHldr M Conservative
10265 (02bree1:074) protections (ab) (ab) ABC 40 nonAdvDgreHldr F Liberal
10266 (02bree1:075) protest (ev) (ev) C_S 18 AdvDgreHldr M Liberal
10267 (02bree1:076) protest (ev) (ev) C_S 4 AdvDgreHldr M Liberal
10268 (02bree1:077) Protest (ev) (ev) C_S 17 AdvDgreHldr F Moderate
10269 (02bree1:078) protest (ev) (ev) C_S 38 nonAdvDgreHldr F Liberal
10270 (02bree1:079) protest (ev) (ev) C_S 1 nonAdvDgreHldr F Moderate
10271 (02bree1:080) protest (ev) (ev) C_S 24 nonAdvDgreHldr F Moderate
253
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10272 (02bree1:081) Reform (ab) (ab) ABC 43 AdvDgreHldr F Liberal
10273 (02bree1:082) Reform (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10274 (02bree1:083) revolution (ab) (ab) ABC 18 AdvDgreHldr M Liberal
10275 (02bree1:084) ridiculous (at) (at) ABC 5 nonAdvDgreHldr M Conservative
10276 (02bree1:085) rights (ab) (ab) ABC 40 nonAdvDgreHldr F Liberal
10277 (02bree1:086) sad (at) (at) ABC 8 nonAdvDgreHldr F Moderate
10278 (02bree1:087) San Diego Union-Tribune (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
10279 (02bree1:089) sea life (ob) (ob) LOB 17 AdvDgreHldr F Moderate
10280 (02bree1:090) sea lion (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10281 (02bree1:091) Sea World (se) (se) C_S 9 AdvDgreHldr F Liberal
10282 (02bree1:091) Sea World (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10283 (02bree1:092) Sea World (se) (se) C_S 35 AdvDgreHldr F Moderate
10284 (02bree1:092) Sea World (tx) (tx) LOB 35 AdvDgreHldr F Moderate
10285 (02bree1:093) sea world (se) (se) C_S 4 AdvDgreHldr M Liberal
10286 (02bree1:093) sea world (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10287 (02bree1:094) sea world (se) (se) C_S 25 nonAdvDgreHldr F Conservative
10288 (02bree1:094) sea world (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10289 (02bree1:095) sealife (ob) (ob) LOB 17 AdvDgreHldr F Moderate
10290 (02bree1:096) Seaworld (se) (se) C_S 38 nonAdvDgreHldr F Liberal
10291 (02bree1:096) Seaworld (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10292 (02bree1:097) serious issue (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10293 (02bree1:098) Shamu (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10294 (02bree1:099) shamu (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10295 (02bree1:100) Shark tank (WTF) (WTF) WTF 39 nonAdvDgreHldr M Conservative
10296 (02bree1:101) sharks (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10297 (02bree1:102) silly (at) (at) ABC 12 nonAdvDgreHldr F Moderate
10298 (02bree1:104) turtle (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10299 (02bree1:105) typical PETA member (pe) (pe) PEO 36 nonAdvDgreHldr M Liberal
10300 (02bree1:105) typical PETA member (ss) (ss) PRA 36 nonAdvDgreHldr M Liberal
10301 (02bree1:106) what else can we “occupy”? (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10302 (02bree1:107) white man ahead (pe) (pe) PEO 2 nonAdvDgreHldr F Conservative
10303 (02bree1:107) white man ahead (ss) (ss) PRA 2 nonAdvDgreHldr F Conservative
10304 (02bree1:900) 99% (rf) (rf) ERE 21 nonAdvDgreHldr F Liberal
10305 (02bree1:901) animal cruelty (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10306 (02bree1:902) animal cruelty (th) (th) ABC 27 nonAdvDgreHldr M Liberal
254
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10307 (02bree1:903) animal rights ppl are weird (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10308 (02bree1:904) deta (WTF) (WTF) WTF 32 nonAdvDgreHldr F Conservative
10309 (02bree1:905) Dolphins (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10310 (02bree1:906) everyone deserves a voice (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
10311 (02bree1:907) extremist (pe) (pe) PEO 11 nonAdvDgreHldr M Liberal
10312 (02bree1:908) fight the power (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10313 (02bree1:909) fight the power (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10314 (02bree1:910) free (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10315 (02bree1:911) freedom (th) (th) ABC 27 nonAdvDgreHldr M Liberal
10316 (02bree1:912) Hippies (pe) (pe) PEO 30 nonAdvDgreHldr M Conservative
10317 (02bree1:913) Occupy (th) (th) ABC 21 nonAdvDgreHldr F Liberal
10318 (02bree1:913) Occupy (tx) (tx) LOB 21 nonAdvDgreHldr F Liberal
10319 (02bree1:915) Occupy Wall Street (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10320 (02bree1:916) Peta (ss) (ss) PRA 30 nonAdvDgreHldr M Conservative
10321 (02bree1:916) Peta (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10322 (02bree1:918) PETA (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
10323 (02bree1:918) PETA (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10324 (02bree1:920) pointless (at) (at) ABC 42 nonAdvDgreHldr M Moderate
10325 (02bree1:920) pointless (pr) (pr) VRE 42 nonAdvDgreHldr M Moderate
10326 (02bree1:921) Prosters (pe) (pe) PEO 23 nonAdvDgreHldr F Liberal
10327 (02bree1:922) protest (se) (se) C_S 13 nonAdvDgreHldr F Moderate
10328 (02bree1:923) Sea (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10329 (02bree1:924) sea world (se) (se) C_S 32 nonAdvDgreHldr F Conservative
10330 (02bree1:924) sea world (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10331 (02bree1:926) Shamu (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10332 (02bree1:927) shamu (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10333 (02bree1:928) slaves (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10334 (02bree1:929) slaves (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10335 (02bree1:930) Stupid (at) (at) ABC 30 nonAdvDgreHldr M Conservative
10336 (02bree1:930) Stupid (pr) (pr) VRE 30 nonAdvDgreHldr M Conservative
10337 (02bree1:931) those fish live better than us (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10338 (02bree1:932) wall street (tx) (tx) LOB 13 nonAdvDgreHldr F Moderate
10339 (03hand1:001) admissions (th) (th) ABC 9 AdvDgreHldr F Liberal
10340 (03hand1:002) after 9/11 (at) (at) ABC 2 nonAdvDgreHldr F Conservative
10341 (03hand1:003) annoying (at) (at) ABC 15 nonAdvDgreHldr F Liberal
255
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10342 (03hand1:004) annoying (at) (at) ABC 12 nonAdvDgreHldr F Moderate
10343 (03hand1:005) cheating (th) (th) ABC 9 AdvDgreHldr F Liberal
10344 (03hand1:006) cheating (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10345 (03hand1:007) civil liberties (th) (th) ABC 18 AdvDgreHldr M Liberal
10346 (03hand1:008) college (th) (th) ABC 9 AdvDgreHldr F Liberal
10347 (03hand1:009) College Entrance Exams (ev) (ev) C_S 16 AdvDgreHldr M Moderate
10348 (03hand1:010) Deceit (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10349 (03hand1:011) difficult (at) (at) ABC 1 nonAdvDgreHldr F Moderate
10350 (03hand1:012) difficult (at) (at) ABC 1 nonAdvDgreHldr F Moderate
10351 (03hand1:013) dreadful (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10352 (03hand1:015) Education (th) (th) ABC 16 AdvDgreHldr M Moderate
10353 (03hand1:016) education (th) (th) ABC 17 AdvDgreHldr F Moderate
10354 (03hand1:017) feelings of not being safe (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10355 (03hand1:017) feelings of not being safe (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10356 (03hand1:018) funny and true (ab) (ab) ABC 5 nonAdvDgreHldr M Conservative
10357 (03hand1:019) guys are dumb (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10358 (03hand1:019) guys are dumb (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10359 (03hand1:020) high school (th) (th) ABC 4 AdvDgreHldr M Liberal
10360 (03hand1:021) high school (th) (th) ABC 9 AdvDgreHldr F Liberal
10361 (03hand1:022) high school (th) (th) ABC 17 AdvDgreHldr F Moderate
10362 (03hand1:023) high school (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10363 (03hand1:024) High School (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10364 (03hand1:025) high school underachievement (th) (th) ABC 19 AdvDgreHldr F Moderate
10365 (03hand1:026) homeland security (th) (th) ABC 10 nonAdvDgreHldr M Moderate
10366 (03hand1:027) i don\'t get it (un) (un) VRE 34 nonAdvDgreHldr M Moderate
10367 (03hand1:028) Let's hope the test is easier to get through than the security (th) (th) ABC 33 nonAdvDgreHldr F Moderate
10368 (03hand1:028) Let's hope the test is easier to get through than the security (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10369 (03hand1:031) metal detectors (WTF) (WTF) WTF 4 AdvDgreHldr M Liberal
10370 (03hand1:033) National Security (th) (th) ABC 43 AdvDgreHldr F Liberal
10371 (03hand1:034) nervous (em) (em) PRA 24 nonAdvDgreHldr F Moderate
10372 (03hand1:035) Newsday (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
10373 (03hand1:035) Newsday (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10374 (03hand1:036) [not very funny], confusing (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
10375 (03hand1:036) not very funny, [confusing] (un) (un) VRE 34 nonAdvDgreHldr M Moderate
10376 (03hand1:037) overdone (at) (at) ABC 42 nonAdvDgreHldr M Moderate
256
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10377 (03hand1:038) overuse of security (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10378 (03hand1:040) pat downs (WTF) (WTF) WTF 10 nonAdvDgreHldr M Moderate
10379 (03hand1:041) pressure (th) (th) ABC 25 nonAdvDgreHldr F Conservative
10380 (03hand1:041) pressure (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10381 (03hand1:042) Pressure (th) (th) ABC 37 nonAdvDgreHldr M Moderate
10382 (03hand1:042) Pressure (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10383 (03hand1:043) privacy (th) (th) ABC 18 AdvDgreHldr M Liberal
10384 (03hand1:044) questions (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10385 (03hand1:045) SAT (th) (th) ABC 4 AdvDgreHldr M Liberal
10386 (03hand1:045) SAT (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10387 (03hand1:046) SAT (th) (th) ABC 9 AdvDgreHldr F Liberal
10388 (03hand1:046) SAT (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10389 (03hand1:047) SAT (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10390 (03hand1:047) SAT (tx) (tx) LOB 7 nonAdvDgreHldr M Moderate
10391 (03hand1:048) sat (th) (th) ABC 26 nonAdvDgreHldr M Conservative
10392 (03hand1:048) sat (tx) (tx) LOB 26 nonAdvDgreHldr M Conservative
10393 (03hand1:049) SAT testing (ev) (ev) C_S 35 AdvDgreHldr F Moderate
10394 (03hand1:049) SAT testing (tx) (tx) LOB 35 AdvDgreHldr F Moderate
10395 (03hand1:050) Sat testing (ev) (ev) C_S 19 AdvDgreHldr F Moderate
10396 (03hand1:050) Sat testing (tx) (tx) LOB 19 AdvDgreHldr F Moderate
10397 (03hand1:051) SAT Testing (ev) (ev) C_S 39 nonAdvDgreHldr M Conservative
10398 (03hand1:051) SAT Testing (tx) (tx) LOB 39 nonAdvDgreHldr M Conservative
10399 (03hand1:052) SAT testing (ev) (ev) C_S 20 nonAdvDgreHldr M Moderate
10400 (03hand1:052) SAT testing (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10401 (03hand1:053) Scams (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10402 (03hand1:054) school security (th) (th) ABC 35 AdvDgreHldr F Moderate
10403 (03hand1:055) school shootings (th) (th) ABC 26 nonAdvDgreHldr M Conservative
10404 (03hand1:056) screening (ev) (ev) C_S 4 AdvDgreHldr M Liberal
10405 (03hand1:056) screening (th) (th) ABC 4 AdvDgreHldr M Liberal
10406 (03hand1:057) securing EVERYTHING now (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10407 (03hand1:057) securing EVERYTHING now (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10408 (03hand1:058) security (th) (th) ABC 16 AdvDgreHldr M Moderate
10409 (03hand1:058) security (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10410 (03hand1:059) security (th) (th) ABC 18 AdvDgreHldr M Liberal
10411 (03hand1:059) security (tx) (tx) LOB 18 AdvDgreHldr M Liberal
257
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10412 (03hand1:060) Security (th) (th) ABC 4 AdvDgreHldr M Liberal
10413 (03hand1:060) Security (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10414 (03hand1:061) security (th) (th) ABC 9 AdvDgreHldr F Liberal
10415 (03hand1:061) security (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10416 (03hand1:062) security (th) (th) ABC 25 nonAdvDgreHldr F Conservative
10417 (03hand1:062) security (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10418 (03hand1:063) security (th) (th) ABC 33 nonAdvDgreHldr F Moderate
10419 (03hand1:063) security (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10420 (03hand1:064) security (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10421 (03hand1:064) security (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
10422 (03hand1:066) Security is a big pain (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
10423 (03hand1:066) Security is a big pain (th) (th) ABC 29 nonAdvDgreHldr F Conservative
10424 (03hand1:067) Security joke (ca) (ca) C_S 39 nonAdvDgreHldr M Conservative
10425 (03hand1:068) security screening (ev) (ev) C_S 18 AdvDgreHldr M Liberal
10426 (03hand1:068) security screening (th) (th) ABC 18 AdvDgreHldr M Liberal
10427 (03hand1:069) slackers (pe) (pe) PEO 6 nonAdvDgreHldr M Conservative
10428 (03hand1:069) slackers (ss) (ss) PRA 6 nonAdvDgreHldr M Conservative
10429 (03hand1:070) standardized testing (th) (th) ABC 4 AdvDgreHldr M Liberal
10430 (03hand1:071) stress (th) (th) ABC 9 AdvDgreHldr F Liberal
10431 (03hand1:072) stressful (at) (at) ABC 40 nonAdvDgreHldr F Liberal
10432 (03hand1:073) stressful (at) (at) ABC 12 nonAdvDgreHldr F Moderate
10433 (03hand1:074) students (pe) (pe) PEO 24 nonAdvDgreHldr F Moderate
10434 (03hand1:074) students (ss) (ss) PRA 24 nonAdvDgreHldr F Moderate
10435 (03hand1:075) students cheating on SAT? (cn) (cn) VRE 34 nonAdvDgreHldr M Moderate
10436 (03hand1:076) such a process (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10437 (03hand1:077) teenagers (pe) (pe) PEO 9 AdvDgreHldr F Liberal
10438 (03hand1:077) teenagers (ss) (ss) PRA 9 AdvDgreHldr F Liberal
10439 (03hand1:078) teens (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10440 (03hand1:078) teens (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10441 (03hand1:079) Terrorism (th) (th) ABC 18 AdvDgreHldr M Liberal
10442 (03hand1:080) terrorism (th) (th) ABC 43 AdvDgreHldr F Liberal
10443 (03hand1:083) testing (ev) (ev) C_S 9 AdvDgreHldr F Liberal
10444 (03hand1:084) testing (ev) (ev) C_S 17 AdvDgreHldr F Moderate
10445 (03hand1:085) testing (ev) (ev) C_S 25 nonAdvDgreHldr F Conservative
10446 (03hand1:086) testing (ev) (ev) C_S 1 nonAdvDgreHldr F Moderate
258
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10447 (03hand1:087) thankfully it is over (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10448 (03hand1:089) Tricky Wording! Endless Questions! [Unbelievable Pressure!] (at) (at) ABC 33 nonAdvDgreHldr F Moderate
10449 (03hand1:089) Tricky Wording! Endless Questions! Unbelievable Pressure! (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10450 (03hand1:090) Tricky Wording! Endless Questions! [Unbelievable Pressure!] (at) (at) ABC 33 nonAdvDgreHldr F Moderate
10451 (03hand1:090) Tricky Wording! Endless Questions! Unbelievable Pressure! (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10452 (03hand1:091) Tricky Wording! Endless Questions! [Unbelievable Pressure!] (at) (at) ABC 33 nonAdvDgreHldr F Moderate
10453 (03hand1:091) Tricky Wording! Endless Questions! Unbelievable Pressure! (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10454 (03hand1:092) ugh high school (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
10455 (03hand1:093) Unjust (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10456 (03hand1:094) Unnecessary (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10457 (03hand1:095) unnecessary hurdles (pr) (pr) VRE 40 nonAdvDgreHldr F Liberal
10458 (03hand1:095) unnecessary hurdles (th) (th) ABC 40 nonAdvDgreHldr F Liberal
10459 (03hand1:096) unprepared (em) (em) PRA 6 nonAdvDgreHldr M Conservative
10460 (03hand1:097) Walt Handelsman (ar) (ar) AHI 4 AdvDgreHldr M Liberal
10461 (03hand1:097) Walt Handelsman (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10462 (03hand1:098) What security? (cn) (cn) VRE 6 nonAdvDgreHldr M Conservative
10463 (03hand1:099) Wo[r]ding (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10464 (03hand1:100) Wording (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10465 (03hand1:101) worried (em) (em) PRA 15 nonAdvDgreHldr F Liberal
10466 (03hand1:102) worried (em) (em) PRA 24 nonAdvDgreHldr F Moderate
10467 (03hand1:103) TRUE (pr) (pr) VRE 8 nonAdvDgreHldr F Moderate
10468 (03hand1:900) actually true (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10469 (03hand1:902) Easier Than Security (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10470 (03hand1:903) Endless Questions! (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10471 (03hand1:904) excessive (at) (at) ABC 21 nonAdvDgreHldr F Liberal
10472 (03hand1:904) excessive (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10473 (03hand1:905) external preassure to succeed (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10474 (03hand1:906) long (ab) (ab) ABC 12 nonAdvDgreHldr F Moderate
10475 (03hand1:907) long wait (ab) (ab) ABC 15 nonAdvDgreHldr F Liberal
10476 (03hand1:908) LSAT (rf) (rf) ERE 27 nonAdvDgreHldr M Liberal
10477 (03hand1:909) Making even harder to do the things you don't even want to do (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
10478 (03hand1:910) MTA (rf) (rf) ERE 10 nonAdvDgreHldr M Moderate
10479 (03hand1:911) Nervous (at) (at) ABC 27 nonAdvDgreHldr M Liberal
10480 (03hand1:912) Overkill (at) (at) ABC 23 nonAdvDgreHldr F Liberal
10481 (03hand1:912) Overkill (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
259
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10482 (03hand1:913) passing security is harder (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
10483 (03hand1:914) SAT (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10484 (03hand1:914) SAT (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10485 (03hand1:915) SAT (th) (th) ABC 32 nonAdvDgreHldr F Conservative
10486 (03hand1:915) SAT (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10487 (03hand1:916) security (th) (th) ABC 15 nonAdvDgreHldr F Liberal
10488 (03hand1:916) security (tx) (tx) LOB 15 nonAdvDgreHldr F Liberal
10489 (03hand1:917) Security (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10490 (03hand1:917) Security (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10491 (03hand1:918) Security Checkpoints (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10492 (03hand1:919) security issues (th) (th) ABC 21 nonAdvDgreHldr F Liberal
10493 (03hand1:920) terrorists (rf) (rf) ERE 13 nonAdvDgreHldr F Moderate
10494 (03hand1:921) Test (th) (th) ABC 37 nonAdvDgreHldr M Moderate
10495 (03hand1:922) test are easier than security (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
10496 (03hand1:922) test are easier than security (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10497 (03hand1:923) the way it is (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
10498 (03hand1:924) thems (WTF) (WTF) WTF 41 AdvDgreHldr F Liberal
10499 (03hand1:926) to get through than the (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10500 (03hand1:927) TSA (rf) (rf) ERE 23 nonAdvDgreHldr F Liberal
10501 (03hand1:928) Unbelievable Pressure! (at) (at) ABC 33 nonAdvDgreHldr F Moderate
10502 (03hand1:928) Unbelievable Pressure! (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10503 (03hand1:929) unfair to good citizens (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
10504 (03hand1:930) weight put on stanardized test (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10505 (04luck1:001) “Mission Accomplished” (rf) (rf) ERE 16 AdvDgreHldr M Moderate
10506 (04luck1:001) “Mission Accomplished” (th) (th) ABC 16 AdvDgreHldr M Moderate
10507 (04luck1:002) “Mission Accomplished” (rf) (rf) ERE 39 nonAdvDgreHldr M Conservative
10508 (04luck1:002) “Mission Accomplished” (th) (th) ABC 39 nonAdvDgreHldr M Conservative
10509 (04luck1:003) accomplished (tx) (tx) LOB 25 AdvDgreHldr F Conservative
10510 (04luck1:004) Accomplished (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10511 (04luck1:005) accomplished (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10512 (04luck1:007) [Afghanistan] political cartoon (th) (th) ABC 35 AdvDgreHldr F Moderate
10513 (04luck1:007) Afghanistan [political cartoon] (fo) (fo) AHI 35 AdvDgreHldr F Moderate
10514 (04luck1:008) aircraft carrier (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10515 (04luck1:009) Aircraft Carrier (ob) (ob) LOB 1 nonAdvDgreHldr F Moderate
10516 (04luck1:010) Atlanta Journal-Constitution (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
260
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10517 (04luck1:010) Atlanta Journal-Constitution (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10518 (04luck1:011) b/c size laura bush is boss (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10519 (04luck1:011) b/c size laura bush is boss (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10520 (04luck1:012) banner (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10521 (04luck1:012) banner (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10522 (04luck1:013) Banner (ob) (ob) LOB 1 nonAdvDgreHldr F Moderate
10523 (04luck1:013) Banner (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
10524 (04luck1:014) Barack Obama (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10525 (04luck1:015) Barney (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10526 (04luck1:016) bias (ab) (ab) ABC 10 nonAdvDgreHldr M Moderate
10527 (04luck1:017) bitter (em) (em) PRA 4 AdvDgreHldr M Liberal
10528 (04luck1:018) blunder (ab) (ab) ABC 25 nonAdvDgreHldr F Conservative
10529 (04luck1:018) blunder (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10530 (04luck1:019) Bring Our Troops Home (pr) (pr) VRE 22 nonAdvDgreHldr F Moderate
10531 (04luck1:019) Bring Our Troops Home (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10532 (04luck1:020) Bush (pe) (pe) PEO 9 AdvDgreHldr F Liberal
10533 (04luck1:021) bush (pe) (pe) PEO 18 AdvDgreHldr M Liberal
10534 (04luck1:022) Bush (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10535 (04luck1:023) bush (pe) (pe) PEO 17 AdvDgreHldr F Moderate
10536 (04luck1:024) Bush (pe) (pe) PEO 38 nonAdvDgreHldr F Liberal
10537 (04luck1:025) Bush (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
10538 (04luck1:026) Bush (pe) (pe) PEO 1 nonAdvDgreHldr F Moderate
10539 (04luck1:027) bush (pe) (pe) PEO 15 nonAdvDgreHldr F Liberal
10540 (04luck1:028) bush (pe) (pe) PEO 26 nonAdvDgreHldr M Conservative
10541 (04luck1:029) bush critiquing obama (ac) (ac) C_S 2 nonAdvDgreHldr F Conservative
10542 (04luck1:030) Bush must feel better (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
10543 (04luck1:030) Bush must feel better (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10544 (04luck1:031) bush v. obama (ab) (ab) ABC 9 AdvDgreHldr F Liberal
10545 (04luck1:032) Bush's legacy (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10546 (04luck1:033) Critical of Bush (pr) (pr) VRE 16 AdvDgreHldr M Moderate
10547 (04luck1:033) Critical of Bush (th) (th) ABC 16 AdvDgreHldr M Moderate
10548 (04luck1:034) debate on war on terror (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10549 (04luck1:034) debate on war on terror (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10550 (04luck1:035) deceit (ab) (ab) ABC 10 nonAdvDgreHldr M Moderate
10551 (04luck1:036) differing views on the war (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
261
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10552 (04luck1:036) differing views on the war (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10553 (04luck1:037) division (at) (at) ABC 24 nonAdvDgreHldr F Moderate
10554 (04luck1:038) dumb (em) (em) PRA 5 nonAdvDgreHldr M Conservative
10555 (04luck1:039) dumbass (em) (em) PRA 40 nonAdvDgreHldr F Liberal
10556 (04luck1:040) exit (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10557 (04luck1:041) exit (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10558 (04luck1:042) Exit (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10559 (04luck1:043) finally (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10560 (04luck1:044) foreign policy (th) (th) ABC 18 AdvDgreHldr M Liberal
10561 (04luck1:045) G.W. Bush (pe) (pe) PEO 19 AdvDgreHldr F Moderate
10562 (04luck1:046) George Bush (pe) (pe) PEO 35 AdvDgreHldr F Moderate
10563 (04luck1:047) george bush (pe) (pe) PEO 7 nonAdvDgreHldr M Moderate
10564 (04luck1:048) George W. Bush (pe) (pe) PEO 30 nonAdvDgreHldr M Conservative
10565 (04luck1:049) George W. Bush (pe) (pe) PEO 43 AdvDgreHldr F Liberal
10566 (04luck1:050) hatred between obama and bush (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
10567 (04luck1:050) hatred between obama and bush (th) (th) ABC 29 nonAdvDgreHldr F Conservative
10568 (04luck1:051) illegal war (th) (th) ABC 40 nonAdvDgreHldr F Liberal
10569 (04luck1:052) Iraq (th) (th) ABC 9 AdvDgreHldr F Liberal
10570 (04luck1:052) Iraq (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10571 (04luck1:053) iraq (th) (th) ABC 16 AdvDgreHldr M Moderate
10572 (04luck1:053) iraq (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10573 (04luck1:054) Iraq (th) (th) ABC 18 AdvDgreHldr M Liberal
10574 (04luck1:054) Iraq (tx) (tx) LOB 18 AdvDgreHldr M Liberal
10575 (04luck1:055) iraq (th) (th) ABC 4 AdvDgreHldr M Liberal
10576 (04luck1:055) iraq (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10577 (04luck1:056) Iraq (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10578 (04luck1:056) Iraq (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
10579 (04luck1:057) Iraq War (th) (th) ABC 43 AdvDgreHldr F Liberal
10580 (04luck1:058) Iraq War (th) (th) ABC 4 AdvDgreHldr M Liberal
10581 (04luck1:059) Iraq War (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10582 (04luck1:060) judgemental (em) (em) PRA 15 nonAdvDgreHldr F Liberal
10583 (04luck1:061) Laura Bush (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10584 (04luck1:062) liberal [cartoon] (fo) (fo) AHI 6 nonAdvDgreHldr M Conservative
10585 (04luck1:062) liberal cartoon (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10586 (04luck1:063) loss of focus (at) (at) ABC 42 nonAdvDgreHldr M Moderate
262
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10587 (04luck1:064) Luckovich (ar) (ar) AHI 4 AdvDgreHldr M Liberal
10588 (04luck1:064) Luckovich (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10589 (04luck1:065) makes bush look like an idiot (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10590 (04luck1:066) Man of Words (pr) (pr) VRE 39 nonAdvDgreHldr M Conservative
10591 (04luck1:067) Marketing at its best (pr) (pr) VRE 39 nonAdvDgreHldr M Conservative
10592 (04luck1:067) Marketing at its best (th) (th) ABC 39 nonAdvDgreHldr M Conservative
10593 (04luck1:068) mission (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10594 (04luck1:069) Mission (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10595 (04luck1:070) Mission (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10596 (04luck1:071) mission (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10597 (04luck1:072) mission (tx) (tx) LOB 26 nonAdvDgreHldr M Conservative
10598 (04luck1:073) Mission Accomplished (rf) (rf) ERE 9 AdvDgreHldr F Liberal
10599 (04luck1:073) Mission Accomplished (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10600 (04luck1:074) Mission Accomplished (rf) (rf) ERE 35 AdvDgreHldr F Moderate
10601 (04luck1:074) Mission Accomplished (tx) (tx) LOB 35 AdvDgreHldr F Moderate
10602 (04luck1:075) Mission Accomplished (rf) (rf) ERE 4 AdvDgreHldr M Liberal
10603 (04luck1:075) Mission Accomplished (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10604 (04luck1:076) mission accomplished (rf) (rf) ERE 38 nonAdvDgreHldr F Liberal
10605 (04luck1:076) mission accomplished (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10606 (04luck1:077) mission accomplished (rf) (rf) ERE 23 nonAdvDgreHldr F Liberal
10607 (04luck1:077) mission accomplished (tx) (tx) LOB 23 nonAdvDgreHldr F Liberal
10608 (04luck1:078) new election (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10609 (04luck1:079) no idea what is going on (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
10610 (04luck1:079) no idea what is going on (th) (th) ABC 12 nonAdvDgreHldr F Moderate
10611 (04luck1:080) Obama (pe) (pe) PEO 9 AdvDgreHldr F Liberal
10612 (04luck1:081) Obama (pe) (pe) PEO 16 AdvDgreHldr M Moderate
10613 (04luck1:082) obama (pe) (pe) PEO 18 AdvDgreHldr M Liberal
10614 (04luck1:083) Obama (pe) (pe) PEO 43 AdvDgreHldr F Liberal
10615 (04luck1:084) Obama (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10616 (04luck1:085) obama (pe) (pe) PEO 38 nonAdvDgreHldr F Liberal
10617 (04luck1:086) obama (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
10618 (04luck1:087) Obama (pe) (pe) PEO 1 nonAdvDgreHldr F Moderate
10619 (04luck1:088) obama (pe) (pe) PEO 15 nonAdvDgreHldr F Liberal
10620 (04luck1:089) Obama's legacy (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10621 (04luck1:090) plans are usually unsucessful (pr) (pr) VRE 8 nonAdvDgreHldr F Moderate
263
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10622 (04luck1:090) plans are usually unsucessful (th) (th) ABC 8 nonAdvDgreHldr F Moderate
10623 (04luck1:091) Politics (th) (th) ABC 26 nonAdvDgreHldr M Conservative
10624 (04luck1:092) Presidency (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10625 (04luck1:093) probably happened (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10626 (04luck1:094) propaganda (ab) (ab) ABC 10 nonAdvDgreHldr M Moderate
10627 (04luck1:095) republican (ss) (ss) PRA 17 AdvDgreHldr F Moderate
10628 (04luck1:096) Soldiers (PEO) (PEO) PEO 22 nonAdvDgreHldr F Moderate
10629 (04luck1:097) standard Obama [banner] (ob) (ob) LOB 6 nonAdvDgreHldr M Conservative
10630 (04luck1:097) standard Obama banner (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10631 (04luck1:098) Terrorism (th) (th) ABC 43 AdvDgreHldr F Liberal
10632 (04luck1:099) Troops (PEO) (PEO) PEO 22 nonAdvDgreHldr F Moderate
10633 (04luck1:100) unfair (at) (at) ABC 40 nonAdvDgreHldr F Liberal
10634 (04luck1:101) W (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10635 (04luck1:102) war (th) (th) ABC 9 AdvDgreHldr F Liberal
10636 (04luck1:103) war (th) (th) ABC 4 AdvDgreHldr M Liberal
10637 (04luck1:104) war (th) (th) ABC 26 nonAdvDgreHldr M Conservative
10638 (04luck1:105) War (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10639 (04luck1:106) War in Afghanistan (pr) (pr) VRE 19 AdvDgreHldr F Moderate
10640 (04luck1:106) War in Afghanistan (th) (th) ABC 19 AdvDgreHldr F Moderate
10641 (04luck1:107) what mission? (un) (un) VRE 12 nonAdvDgreHldr F Moderate
10642 (04luck1:900) accomplished (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10643 (04luck1:901) Afghanistan (th) (th) ABC 17 AdvDgreHldr F Moderate
10644 (04luck1:902) bush (pe) (pe) PEO 13 nonAdvDgreHldr F Moderate
10645 (04luck1:903) Bush (pe) (pe) PEO 21 nonAdvDgreHldr F Liberal
10646 (04luck1:904) Bushism (rf) (rf) ERE 23 nonAdvDgreHldr F Liberal
10647 (04luck1:905) George W. Bush (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10648 (04luck1:906) good [rhetoric] (ca) (ca) C_S 27 nonAdvDgreHldr M Liberal
10649 (04luck1:906) good rhetoric (pr) (pr) VRE 27 nonAdvDgreHldr M Liberal
10650 (04luck1:907) Insult (ac) (ac) C_S 11 nonAdvDgreHldr M Liberal
10651 (04luck1:908) Iraq War (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10652 (04luck1:909) Keeping concerned with the things that don't matter(pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
10653 (04luck1:910) Making Bush look like a child (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10654 (04luck1:911) misleading (at) (at) ABC 13 nonAdvDgreHldr F Moderate
10655 (04luck1:911) misleading (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
10656 (04luck1:912) mission (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
264
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10658 (04luck1:913) mission accomplished (rf) (rf) ERE 7 nonAdvDgreHldr M Moderate
10657 (04luck1:913) mission accomplished (tx) (tx) LOB 7 nonAdvDgreHldr M Moderate
10659 (04luck1:914) Obama (pe) (pe) PEO 30 nonAdvDgreHldr M Conservative
10660 (04luck1:915) obama (pe) (pe) PEO 13 nonAdvDgreHldr F Moderate
10661 (04luck1:916) obama criticism (ab) (ab) ABC 21 nonAdvDgreHldr F Liberal
10662 (04luck1:916) obama criticism (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10663 (04luck1:917) Subliminal (WTF) (WTF) WTF 11 nonAdvDgreHldr M Liberal
10664 (04luck1:918) Terrorism (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10665 (05rami1:001) 747 (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10666 (05rami1:002) Air Force One (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10667 (05rami1:002) Air Force One (se) (se) C_S 4 AdvDgreHldr M Liberal
10668 (05rami1:003) Air Force One (ob) (ob) LOB 7 nonAdvDgreHldr M Moderate
10669 (05rami1:003) Air Force One (se) (se) C_S 7 nonAdvDgreHldr M Moderate
10670 (05rami1:004) Air Force One (ob) (ob) LOB 9 AdvDgreHldr F Liberal
10671 (05rami1:004) Air Force One (se) (se) C_S 9 AdvDgreHldr F Liberal
10672 (05rami1:005) Air Force One (ob) (ob) LOB 16 AdvDgreHldr M Moderate
10673 (05rami1:005) Air Force One (se) (se) C_S 16 AdvDgreHldr M Moderate
10674 (05rami1:006) air force one (ob) (ob) LOB 38 nonAdvDgreHldr F Liberal
10675 (05rami1:006) air force one (se) (se) C_S 38 nonAdvDgreHldr F Liberal
10676 (05rami1:007) air force one (ob) (ob) LOB 43 AdvDgreHldr F Liberal
10677 (05rami1:007) air force one (se) (se) C_S 43 AdvDgreHldr F Liberal
10678 (05rami1:008) airforce 1 (ob) (ob) LOB 26 nonAdvDgreHldr M Conservative
10679 (05rami1:008) airforce 1 (se) (se) C_S 26 nonAdvDgreHldr M Conservative
10680 (05rami1:009) airforce one (ob) (ob) LOB 1 nonAdvDgreHldr F Moderate
10681 (05rami1:009) airforce one (se) (se) C_S 1 nonAdvDgreHldr F Moderate
10682 (05rami1:010) Al Gore (WTF) (WTF) WTF 6 nonAdvDgreHldr M Conservative
10683 (05rami1:011) American Greed Airline (pr) (pr) VRE 39 nonAdvDgreHldr M Conservative
10684 (05rami1:011) American Greed Airline (th) (th) ABC 39 nonAdvDgreHldr M Conservative
10685 (05rami1:012) americans squander money (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10686 (05rami1:012) americans squander money (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10688 (05rami1:013) americans=wasteful (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10687 (05rami1:013) americans=wasteful (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10689 (05rami1:016) angry public (em) (em) PRA 12 nonAdvDgreHldr F Moderate
10690 (05rami1:016) angry public (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
10691 (05rami1:016) angry public (th) (th) ABC 12 nonAdvDgreHldr F Moderate
265
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10693 (05rami1:017) anti-OBAMA (at) (at) ABC 9 AdvDgreHldr F Liberal
10692 (05rami1:017) anti-OBAMA (pr) (pr) VRE 9 AdvDgreHldr F Liberal
10694 (05rami1:018) Attention (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10695 (05rami1:019) bloated (at) (at) ABC 40 nonAdvDgreHldr F Liberal
10696 (05rami1:020) Campaign Trips (th) (th) ABC 16 AdvDgreHldr M Moderate
10697 (05rami1:021) conservative [cartoon] (fo) (fo) AHI 6 nonAdvDgreHldr M Conservative
10698 (05rami1:021) conservative cartoon (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10699 (05rami1:022) Device (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10700 (05rami1:023) economy (th) (th) ABC 9 AdvDgreHldr F Liberal
10701 (05rami1:024) excesses in government spendin (pr) (pr) VRE 35 AdvDgreHldr F Moderate
10702 (05rami1:024) excesses in government spendin (th) (th) ABC 35 AdvDgreHldr F Moderate
10703 (05rami1:025) extravagent (at) (at) ABC 40 nonAdvDgreHldr F Liberal
10704 (05rami1:026) Flotation (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10705 (05rami1:027) flotation device (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10706 (05rami1:028) flotation device (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10707 (05rami1:029) flying (ac) (ac) C_S 4 AdvDgreHldr M Liberal
10708 (05rami1:030) frivolous (th) (th) ABC 4 AdvDgreHldr M Liberal
10710 (05rami1:030) frivolous (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10709 (05rami1:031) Frivolous (th) (th) ABC 20 nonAdvDgreHldr M Moderate
10711 (05rami1:031) Frivolous (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10712 (05rami1:032) frivolous trip (ev) (ev) C_S 25 nonAdvDgreHldr F Conservative
10713 (05rami1:032) frivolous trip (th) (th) ABC 25 nonAdvDgreHldr F Conservative
10714 (05rami1:032) frivolous trip (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10715 (05rami1:033) Frivolous trip (ev) (ev) C_S 37 nonAdvDgreHldr M Moderate
10716 (05rami1:033) Frivolous trip (th) (th) ABC 37 nonAdvDgreHldr M Moderate
10717 (05rami1:033) Frivolous trip (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10718 (05rami1:034) frustration (at) (at) ABC 12 nonAdvDgreHldr F Moderate
10719 (05rami1:035) government misappropriation (th) (th) ABC 19 AdvDgreHldr F Moderate
10720 (05rami1:036) government spending (th) (th) ABC 18 AdvDgreHldr M Liberal
10721 (05rami1:037) government waste (th) (th) ABC 18 AdvDgreHldr M Liberal
10722 (05rami1:038) Government Waste (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10723 (05rami1:039) government wastefulness (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10724 (05rami1:040) Increased taxes (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
10725 (05rami1:041) Investors Business Daily (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
10726 (05rami1:042) Leno (PEO) (PEO) PEO 30 nonAdvDgreHldr M Conservative
266
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10727 (05rami1:042) Leno (rf) (rf) ERE 4 AdvDgreHldr M Liberal
10728 (05rami1:043) Lettermen (PEO) (PEO) PEO 17 AdvDgreHldr F Moderate
10729 (05rami1:043) Lettermen (rf) (rf) ERE 34 nonAdvDgreHldr M Moderate
10730 (05rami1:044) luxurious (at) (at) ABC 13 nonAdvDgreHldr F Moderate
10731 (05rami1:045) misused money (th) (th) ABC 8 nonAdvDgreHldr F Moderate
10732 (05rami1:046) money waste (th) (th) ABC 9 AdvDgreHldr F Liberal
10733 (05rami1:047) no money in the wallet (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
10734 (05rami1:048) Obama (PEO) (PEO) PEO 4 AdvDgreHldr M Liberal
10735 (05rami1:049) obama (PEO) (PEO) PEO 26 nonAdvDgreHldr M Conservative
10736 (05rami1:050) obama's priorities (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10737 (05rami1:051) our society (ab) (ab) ABC 36 nonAdvDgreHldr M Liberal
10738 (05rami1:051) our society (ss) (ss) PRA 13 nonAdvDgreHldr F Moderate
10739 (05rami1:052) overspending (ab) (ab) ABC 7 nonAdvDgreHldr M Moderate
10740 (05rami1:053) overspending (ab) (ab) ABC 10 nonAdvDgreHldr M Moderate
10741 (05rami1:054) overspending (ab) (ab) ABC 12 nonAdvDgreHldr F Moderate
10742 (05rami1:055) passengers (PEO) (PEO) PEO 4 AdvDgreHldr M Liberal
10743 (05rami1:055) passengers (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10744 (05rami1:055) passengers (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10745 (05rami1:056) pay too much tax (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
10746 (05rami1:056) pay too much tax (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10747 (05rami1:057) plane (ob) (ob) LOB 1 nonAdvDgreHldr F Moderate
10748 (05rami1:058) Plane (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10749 (05rami1:059) President (PEO) (PEO) PEO 16 AdvDgreHldr M Moderate
10750 (05rami1:060) President (PEO) (PEO) PEO 43 AdvDgreHldr F Liberal
10751 (05rami1:061) real world dilemma (pr) (pr) VRE 24 nonAdvDgreHldr F Moderate
10752 (05rami1:062) ridiculous (at) (at) ABC 42 nonAdvDgreHldr M Moderate
10753 (05rami1:063) so true (ab) (ab) ABC 36 nonAdvDgreHldr M Liberal
10754 (05rami1:063) so true (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10755 (05rami1:064) spending (ab) (ab) ABC 4 AdvDgreHldr M Liberal
10756 (05rami1:065) Talk Show (rf) (rf) ERE 43 AdvDgreHldr F Liberal
10757 (05rami1:066) tax payers wallets are bigger (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
10758 (05rami1:066) tax payers wallets are bigger (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10759 (05rami1:067) taxes (ab) (ab) ABC 12 nonAdvDgreHldr F Moderate
10760 (05rami1:068) taxes (ab) (ab) ABC 15 nonAdvDgreHldr F Liberal
10761 (05rami1:069) taxes (ab) (ab) ABC 18 AdvDgreHldr M Liberal
267
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10762 (05rami1:070) taxpayer dollars (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10763 (05rami1:072) taxpayer money (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10764 (05rami1:073) taxpayer pain (th) (th) ABC 35 AdvDgreHldr F Moderate
10765 (05rami1:074) Taxpayer wallets (th) (th) ABC 37 nonAdvDgreHldr M Moderate
10766 (05rami1:074) Taxpayer wallets (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10767 (05rami1:075) taxpayers (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10768 (05rami1:075) taxpayers (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10769 (05rami1:076) Taxpayers (ss) (ss) PRA 9 AdvDgreHldr F Liberal
10770 (05rami1:076) Taxpayers (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10771 (05rami1:077) Taxpayers (ss) (ss) PRA 16 AdvDgreHldr M Moderate
10772 (05rami1:077) Taxpayers (tx) (tx) LOB 16 AdvDgreHldr M Moderate
10773 (05rami1:078) taxpayers (ss) (ss) PRA 20 nonAdvDgreHldr M Moderate
10774 (05rami1:078) taxpayers (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10775 (05rami1:079) taxpayers (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
10776 (05rami1:079) taxpayers (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10777 (05rami1:080) Taxpayers (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
10778 (05rami1:080) Taxpayers (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10779 (05rami1:084) Tonight Show (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10780 (05rami1:085) Tonight Show (tx) (tx) LOB 9 AdvDgreHldr F Liberal
10781 (05rami1:086) tonight show (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10782 (05rami1:087) truth (ab) (ab) ABC 24 nonAdvDgreHldr F Moderate
10783 (05rami1:087) truth (pr) (pr) VRE 24 nonAdvDgreHldr F Moderate
10784 (05rami1:088) unfortunate (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10785 (05rami1:089) Unjust (at) (at) ABC 22 nonAdvDgreHldr F Moderate
10786 (05rami1:090) unnecessary (at) (at) ABC 40 nonAdvDgreHldr F Liberal
10787 (05rami1:091) USA (WTF) (WTF) WTF 1 nonAdvDgreHldr F Moderate
10788 (05rami1:092) usa (WTF) (WTF) WTF 26 nonAdvDgreHldr M Conservative
10789 (05rami1:093) USA (WTF) (WTF) WTF 32 nonAdvDgreHldr F Conservative
10790 (05rami1:094) USA (WTF) (WTF) WTF 33 nonAdvDgreHldr F Moderate
10791 (05rami1:095) wallets (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10792 (05rami1:096) wallets (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10793 (05rami1:097) Wallets (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10794 (05rami1:098) Waste of taxpayer money (th) (th) ABC 43 AdvDgreHldr F Liberal
10795 (05rami1:099) wasted tax dollars (th) (th) ABC 10 nonAdvDgreHldr M Moderate
10796 (05rami1:100) We pay too many taxes (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
268
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10797 (05rami1:100) We pay too many taxes (th) (th) ABC 29 nonAdvDgreHldr F Conservative
10798 (05rami1:101) we value the unimportant (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10799 (05rami1:101) we value the unimportant (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10800 (05rami1:102) we're in for dark times ahead (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
10801 (05rami1:102) we're in for dark times ahead (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10802 (05rami1:103) wrong (ab) (ab) ABC 8 nonAdvDgreHldr F Moderate
10803 (05rami1:104) TRUE (pr) (pr) VRE 5 nonAdvDgreHldr M Conservative
10804 (05rami1:105) TRUE (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10805 (05rami1:900) Airforce One (ob) (ob) LOB 30 nonAdvDgreHldr M Conservative
10806 (05rami1:900) Airforce One (se) (se) C_S 30 nonAdvDgreHldr M Conservative
10807 (05rami1:901) Fatcat gov mentality (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10808 (05rami1:901) Fatcat gov mentality (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10809 (05rami1:902) Government taking advantage (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10810 (05rami1:902) Government taking advantage (th) (th) ABC 21 nonAdvDgreHldr F Liberal
10811 (05rami1:903) Obama (PEO) (PEO) PEO 30 nonAdvDgreHldr M Conservative
10812 (05rami1:904) scary thought because its true (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10813 (05rami1:905) spirit airlines (WTF) (WTF) WTF 27 nonAdvDgreHldr M Liberal
10814 (05rami1:906) taxpayers hurt (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10815 (05rami1:907) Tonight Show (tx) (tx) LOB 43 AdvDgreHldr F Liberal
10816 (05rami1:908) unethical (at) (at) ABC 13 nonAdvDgreHldr F Moderate
10817 (05rami1:908) unethical (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
10818 (05rami1:909) USA (WTF) (WTF) WTF 34 nonAdvDgreHldr M Moderate
10819 (05rami1:910) Wasted Tax Money (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10820 (05rami1:911) wasted taxpayers money (th) (th) ABC 13 nonAdvDgreHldr F Moderate
10821 (05rami1:912) Wasting away 24/7 (th) (th) ABC 31 nonAdvDgreHldr F Conservative
10822 (05rami1:913) Wasting Money (th) (th) ABC 23 nonAdvDgreHldr F Liberal
10823 (05rami1:914) whitty (ab) (ab) ABC 11 nonAdvDgreHldr M Liberal
10824 (06ande2:001) Anderson (ar) (ar) AHI 4 AdvDgreHldr M Liberal
10825 (06ande2:001) Anderson (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10826 (06ande2:002) axes (ob) (ob) LOB 25 nonAdvDgreHldr F Conservative
10827 (06ande2:003) bad loans (ab) (ab) ABC 6 nonAdvDgreHldr M Conservative
10828 (06ande2:004) bail out (ab) (ab) ABC 6 nonAdvDgreHldr M Conservative
10829 (06ande2:005) bail outs (ab) (ab) ABC 6 nonAdvDgreHldr M Conservative
10830 (06ande2:006) both parties clueless (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10831 (06ande2:006) both parties clueless (th) (th) ABC 14 nonAdvDgreHldr F Moderate
269
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10832 (06ande2:007) both republicans and democrats (ss) (ss) PRA 12 nonAdvDgreHldr F Moderate
10833 (06ande2:008) budget (th) (th) ABC 4 AdvDgreHldr M Liberal
10834 (06ande2:009) budget cuts (th) (th) ABC 4 AdvDgreHldr M Liberal
10835 (06ande2:010) butchered (ac) (ac) C_S 40 nonAdvDgreHldr F Liberal
10836 (06ande2:011) Cartoon (fo) (fo) AHI 26 nonAdvDgreHldr M Conservative
10837 (06ande2:012) chopping block (ob) (ob) LOB 17 AdvDgreHldr F Moderate
10838 (06ande2:013) Clever (at) (at) ABC 26 nonAdvDgreHldr M Conservative
10839 (06ande2:014) committee (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
10840 (06ande2:014) committee (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10841 (06ande2:015) Committee (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
10842 (06ande2:015) Committee (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10843 (06ande2:016) Committee (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
10844 (06ande2:016) Committee (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10845 (06ande2:017) Committee (ss) (ss) PRA 33 nonAdvDgreHldr F Moderate
10846 (06ande2:017) Committee (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10847 (06ande2:018) committee not doing anything (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
10848 (06ande2:018) committee not doing anything (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10849 (06ande2:019) Congress (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10850 (06ande2:020) cuts (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10851 (06ande2:021) deficit (th) (th) ABC 43 AdvDgreHldr F Liberal
10852 (06ande2:021) deficit (tx) (tx) LOB 43 AdvDgreHldr F Liberal
10853 (06ande2:022) deficit (th) (th) ABC 4 AdvDgreHldr M Liberal
10854 (06ande2:022) deficit (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10855 (06ande2:023) Deficit (th) (th) ABC 25 nonAdvDgreHldr F Conservative
10856 (06ande2:023) Deficit (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10857 (06ande2:024) Deficit (th) (th) ABC 37 nonAdvDgreHldr M Moderate
10858 (06ande2:024) Deficit (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10859 (06ande2:025) Deficit (th) (th) ABC 38 nonAdvDgreHldr F Liberal
10860 (06ande2:025) Deficit (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
10861 (06ande2:026) deficit (th) (th) ABC 33 nonAdvDgreHldr F Moderate
10862 (06ande2:026) deficit (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
10863 (06ande2:027) Deficit (th) (th) ABC 20 nonAdvDgreHldr M Moderate
10864 (06ande2:027) Deficit (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
10865 (06ande2:028) Deficit (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10866 (06ande2:028) Deficit (th) (th) ABC 30 nonAdvDgreHldr M Conservative
270
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10867 (06ande2:029) deficit committee (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10868 (06ande2:029) deficit committee (th) (th) ABC 4 AdvDgreHldr M Liberal
10869 (06ande2:029) deficit committee (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10870 (06ande2:030) deficit getting big (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
10871 (06ande2:030) deficit getting big (th) (th) ABC 3 nonAdvDgreHldr F Conservative
10872 (06ande2:031) Democrat (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10873 (06ande2:032) Democrat (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
10874 (06ande2:033) Democrats (ss) (ss) PRA 43 AdvDgreHldr F Liberal
10875 (06ande2:034) donkey (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10876 (06ande2:035) economy (th) (th) ABC 4 AdvDgreHldr M Liberal
10877 (06ande2:036) economy (th) (th) ABC 24 nonAdvDgreHldr F Moderate
10878 (06ande2:037) elephant (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10879 (06ande2:038) everyone is to blame (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10880 (06ande2:038) everyone is to blame (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10881 (06ande2:039) governments failed leadership (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10882 (06ande2:039) governments failed leadership (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10883 (06ande2:040) growing too large (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
10884 (06ande2:040) growing too large (th) (th) ABC 10 nonAdvDgreHldr M Moderate
10885 (06ande2:041) Happy Deficiting Day (pr) (pr) VRE 39 nonAdvDgreHldr M Conservative
10886 (06ande2:042) Houston Chronicle (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
10887 (06ande2:042) Houston Chronicle (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10888 (06ande2:043) Independent party (WTF) (WTF) WTF 22 nonAdvDgreHldr F Moderate
10889 (06ande2:044) ineffective (at) (at) ABC 1 nonAdvDgreHldr F Moderate
10890 (06ande2:045) ineptitude (at) (at) ABC 17 AdvDgreHldr F Moderate
10891 (06ande2:046) interesting outlook (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10892 (06ande2:047) large deficit (th) (th) ABC 1 nonAdvDgreHldr F Moderate
10893 (06ande2:048) Moderate (ss) (ss) PRA 22 nonAdvDgreHldr F Moderate
10894 (06ande2:049) no chance they\'ll defeat it (pr) (pr) VRE 8 nonAdvDgreHldr F Moderate
10895 (06ande2:050) noone able to reach conclusion (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
10896 (06ande2:050) noone able to reach conclusion (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10897 (06ande2:051) november (ta) (ta) C_S 15 nonAdvDgreHldr F Liberal
10898 (06ande2:052) obama\'s bail out (th) (th) ABC 6 nonAdvDgreHldr M Conservative
10899 (06ande2:053) overwhelming (at) (at) ABC 4 AdvDgreHldr M Liberal
10900 (06ande2:054) Pilgrims (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10901 (06ande2:055) pilgrims (pe) (pe) PEO 34 nonAdvDgreHldr M Moderate
271
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10902 (06ande2:056) political (at) (at) ABC 6 nonAdvDgreHldr M Conservative
10903 (06ande2:057) political cartoon (fo) (fo) AHI 2 nonAdvDgreHldr F Conservative
10904 (06ande2:058) politics (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10905 (06ande2:059) recession (th) (th) ABC 24 nonAdvDgreHldr F Moderate
10906 (06ande2:060) Republican (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10907 (06ande2:061) Republican (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
10908 (06ande2:062) Republicans (ss) (ss) PRA 21 nonAdvDgreHldr F Liberal
10909 (06ande2:063) rich politicians (ss) (ss) PRA 42 nonAdvDgreHldr M Moderate
10910 (06ande2:063) rich politicians (th) (th) ABC 42 nonAdvDgreHldr M Moderate
10911 (06ande2:064) scary (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10912 (06ande2:065) screwed (at) (at) ABC 12 nonAdvDgreHldr F Moderate
10913 (06ande2:066) Smart (at) (at) ABC 26 nonAdvDgreHldr M Conservative
10914 (06ande2:067) super committee (PEO) (PEO) PEO 7 nonAdvDgreHldr M Moderate
10915 (06ande2:067) super committee (th) (th) ABC 7 nonAdvDgreHldr M Moderate
10916 (06ande2:068) Thanksgiving (ta) (ta) C_S 4 AdvDgreHldr M Liberal
10917 (06ande2:068) Thanksgiving (th) (th) ABC 4 AdvDgreHldr M Liberal
10918 (06ande2:069) thanksgiving (ta) (ta) C_S 13 nonAdvDgreHldr F Moderate
10919 (06ande2:069) thanksgiving (th) (th) ABC 13 nonAdvDgreHldr F Moderate
10920 (06ande2:070) Thanksgiving (ta) (ta) C_S 15 nonAdvDgreHldr F Liberal
10921 (06ande2:070) Thanksgiving (th) (th) ABC 15 nonAdvDgreHldr F Liberal
10922 (06ande2:071) thanksgiving (ta) (ta) C_S 34 nonAdvDgreHldr M Moderate
10923 (06ande2:071) thanksgiving (th) (th) ABC 34 nonAdvDgreHldr M Moderate
10924 (06ande2:073) turkey (ob) (ob) LOB 43 AdvDgreHldr F Liberal
10925 (06ande2:074) turkey (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10926 (06ande2:075) turkey (ob) (ob) LOB 25 nonAdvDgreHldr F Conservative
10927 (06ande2:076) turkey (ob) (ob) LOB 38 nonAdvDgreHldr F Liberal
10928 (06ande2:077) Turkey (ob) (ob) LOB 1 nonAdvDgreHldr F Moderate
10929 (06ande2:078) turkey (ob) (ob) LOB 15 nonAdvDgreHldr F Liberal
10930 (06ande2:079) Turkey 1-Government 0 (pr) (pr) VRE 19 AdvDgreHldr F Moderate
10931 (06ande2:080) turmoil (at) (at) ABC 24 nonAdvDgreHldr F Moderate
10932 (06ande2:081) U.S. deficit-2011 (th) (th) ABC 19 AdvDgreHldr F Moderate
10933 (06ande2:082) Unavoidable debt (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10934 (06ande2:083) unequipped (th) (th) ABC 10 nonAdvDgreHldr M Moderate
10935 (06ande2:085) US economy (th) (th) ABC 4 AdvDgreHldr M Liberal
10936 (06ande2:086) whose to say your better (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
272
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10937 (06ande2:900) Big Deficit (th) (th) ABC 23 nonAdvDgreHldr F Liberal
10938 (06ande2:901) Committee (ss) (ss) PRA 32 nonAdvDgreHldr F Conservative
10939 (06ande2:901) Committee (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10940 (06ande2:902) debt (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10941 (06ande2:903) Deficit (th) (th) ABC 32 nonAdvDgreHldr F Conservative
10942 (06ande2:903) Deficit (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
10943 (06ande2:904) deficit taking over (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
10944 (06ande2:904) deficit taking over (th) (th) ABC 21 nonAdvDgreHldr F Liberal
10945 (06ande2:905) Democrat (ss) (ss) PRA 21 nonAdvDgreHldr F Liberal
10946 (06ande2:906) Dems and GOP can't agree (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
10947 (06ande2:906) Dems and GOP can't agree (th) (th) ABC 23 nonAdvDgreHldr F Liberal
10948 (06ande2:907) large debt (th) (th) ABC 13 nonAdvDgreHldr F Moderate
10949 (06ande2:908) no real effort (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10950 (06ande2:908) no real effort (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10951 (06ande2:909) over their heads (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10952 (06ande2:909) over their heads (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10953 (06ande2:910) overwhelming (at) (at) ABC 11 nonAdvDgreHldr M Liberal
10954 (06ande2:911) Recession (th) (th) ABC 27 nonAdvDgreHldr M Liberal
10955 (06ande2:912) Republicans (ss) (ss) PRA 43 AdvDgreHldr F Liberal
10956 (06ande2:913) Tax cuts (th) (th) ABC 30 nonAdvDgreHldr M Conservative
10957 (06ande2:914) Thanksgiving (ta) (ta) C_S 43 AdvDgreHldr F Liberal
10958 (06ande2:914) Thanksgiving (th) (th) ABC 43 AdvDgreHldr F Liberal
10959 (06ande2:915) the deficit is huge (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
10960 (06ande2:915) the deficit is huge (th) (th) ABC 12 nonAdvDgreHldr F Moderate
10961 (06ande2:916) unequipped for the job (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
10962 (06ande2:916) unequipped for the job (th) (th) ABC 11 nonAdvDgreHldr M Liberal
10963 (06ande2:917) unfortunate (at) (at) ABC 36 nonAdvDgreHldr M Liberal
10964 (06ande2:918) we\'re all in trouble (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
10965 (06ande2:919) Wrong Target (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
10966 (06ande2:920) TRUE (pr) (pr) VRE 5 nonAdvDgreHldr M Conservative
10967 (06ande2:921) TRUE (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
10968 (07bree2:002) Bob Filner (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10969 (07bree2:002) Bob Filner (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10970 (07bree2:003) bob filner (pe) (pe) PEO 7 nonAdvDgreHldr M Moderate
10971 (07bree2:003) bob filner (tx) (tx) LOB 7 nonAdvDgreHldr M Moderate
273
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
10972 (07bree2:004) Bob Filner (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
10973 (07bree2:004) Bob Filner (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
10974 (07bree2:005) Bob Filner (pe) (pe) PEO 30 nonAdvDgreHldr M Conservative
10975 (07bree2:005) Bob Filner (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
10976 (07bree2:006) bob is for it (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
10977 (07bree2:006) bob is for it (th) (th) ABC 2 nonAdvDgreHldr F Conservative
10978 (07bree2:007) Breen (ar) (ar) AHI 4 AdvDgreHldr M Liberal
10979 (07bree2:007) Breen (tx) (tx) LOB 4 AdvDgreHldr M Liberal
10980 (07bree2:008) california (rf) (rf) ERE 7 nonAdvDgreHldr M Moderate
10981 (07bree2:009) california's laws (th) (th) ABC 14 nonAdvDgreHldr F Moderate
10982 (07bree2:010) congress (rf) (rf) ERE 1 nonAdvDgreHldr F Moderate
10983 (07bree2:011) Congressman (pe) (pe) PEO 37 nonAdvDgreHldr M Moderate
10984 (07bree2:011) Congressman (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
10985 (07bree2:011) Congressman (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
10986 (07bree2:012) Congressman Bob Filner (pe) (pe) PEO 43 AdvDgreHldr F Liberal
10987 (07bree2:012) Congressman Bob Filner (ss) (ss) PRA 43 AdvDgreHldr F Liberal
10988 (07bree2:012) Congressman Bob Filner (tx) (tx) LOB 43 AdvDgreHldr F Liberal
10989 (07bree2:013) Congressman Filner (pe) (pe) PEO 19 AdvDgreHldr F Moderate
10990 (07bree2:013) Congressman Filner (ss) (ss) PRA 19 AdvDgreHldr F Moderate
10991 (07bree2:013) Congressman Filner (tx) (tx) LOB 19 AdvDgreHldr F Moderate
10992 (07bree2:014) cop (pe) (pe) PEO 26 nonAdvDgreHldr M Conservative
10993 (07bree2:014) cop (ss) (ss) PRA 26 nonAdvDgreHldr M Conservative
10994 (07bree2:015) cops (pe) (pe) PEO 4 AdvDgreHldr M Liberal
10995 (07bree2:015) cops (ss) (ss) PRA 4 AdvDgreHldr M Liberal
10996 (07bree2:016) cops dont get it (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
10997 (07bree2:017) Discrimination (th) (th) ABC 22 nonAdvDgreHldr F Moderate
10998 (07bree2:018) dope (ob) (ob) LOB 4 AdvDgreHldr M Liberal
10999 (07bree2:018) dope (th) (th) ABC 4 AdvDgreHldr M Liberal
11000 (07bree2:019) Drug legalization (th) (th) ABC 43 AdvDgreHldr F Liberal
11001 (07bree2:020) favored by some (pr) (pr) VRE 8 nonAdvDgreHldr F Moderate
11002 (07bree2:021) high (em) (em) PRA 1 nonAdvDgreHldr F Moderate
11003 (07bree2:022) hippie (pe) (pe) PEO 6 nonAdvDgreHldr M Conservative
11004 (07bree2:022) hippie (ss) (ss) PRA 6 nonAdvDgreHldr M Conservative
11005 (07bree2:023) hippie (pe) (pe) PEO 11 nonAdvDgreHldr M Liberal
11006 (07bree2:023) hippie (ss) (ss) PRA 11 nonAdvDgreHldr M Liberal
274
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11007 (07bree2:024) hippy (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
11008 (07bree2:024) hippy (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11009 (07bree2:025) hippy (pe) (pe) PEO 26 nonAdvDgreHldr M Conservative
11010 (07bree2:025) hippy (ss) (ss) PRA 26 nonAdvDgreHldr M Conservative
11011 (07bree2:026) i hate weed (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11012 (07bree2:027) I think pot should be legalize (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11013 (07bree2:028) Legal marijuana (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11014 (07bree2:028) Legal marijuana (th) (th) ABC 4 AdvDgreHldr M Liberal
11015 (07bree2:029) legalization of marajuana (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11016 (07bree2:030) legalization of marijuana (th) (th) ABC 19 AdvDgreHldr F Moderate
11017 (07bree2:031) Legalize (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11018 (07bree2:032) legalize (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
11019 (07bree2:033) legalize (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
11020 (07bree2:034) Legalize It (th) (th) ABC 20 nonAdvDgreHldr M Moderate
11021 (07bree2:034) Legalize It (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
11022 (07bree2:035) legalize marijuana (th) (th) ABC 2 nonAdvDgreHldr F Conservative
11023 (07bree2:036) Legalize Marijuana (th) (th) ABC 7 nonAdvDgreHldr M Moderate
11024 (07bree2:037) legalize marijuana (th) (th) ABC 38 nonAdvDgreHldr F Liberal
11025 (07bree2:038) legalize marijuanna (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11026 (07bree2:039) legalize weed (th) (th) ABC 15 nonAdvDgreHldr F Liberal
11027 (07bree2:040) Marijuana (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11028 (07bree2:040) Marijuana (th) (th) ABC 4 AdvDgreHldr M Liberal
11029 (07bree2:041) marijuana (ob) (ob) LOB 17 AdvDgreHldr F Moderate
11030 (07bree2:041) marijuana (th) (th) ABC 17 AdvDgreHldr F Moderate
11031 (07bree2:042) Marijuana (ob) (ob) LOB 21 nonAdvDgreHldr F Liberal
11032 (07bree2:042) Marijuana (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11033 (07bree2:043) marijuana (ob) (ob) LOB 22 nonAdvDgreHldr F Moderate
11034 (07bree2:043) marijuana (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11035 (07bree2:045) Mary J (ob) (ob) LOB 39 nonAdvDgreHldr M Conservative
11036 (07bree2:045) Mary J (th) (th) ABC 39 nonAdvDgreHldr M Conservative
11037 (07bree2:046) needs to be legalized already (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
11038 (07bree2:047) NORML (tx) (tx) LOB 7 nonAdvDgreHldr M Moderate
11039 (07bree2:048) not uncommon (ab) (ab) ABC 8 nonAdvDgreHldr F Moderate
11040 (07bree2:049) Occupy (th) (th) ABC 4 AdvDgreHldr M Liberal
11041 (07bree2:049) Occupy (tx) (tx) LOB 4 AdvDgreHldr M Liberal
275
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11042 (07bree2:050) Occupy (th) (th) ABC 38 nonAdvDgreHldr F Liberal
11043 (07bree2:050) Occupy (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
11044 (07bree2:051) Occupy movement (th) (th) ABC 4 AdvDgreHldr M Liberal
11045 (07bree2:052) Occupy Wall Street (th) (th) ABC 4 AdvDgreHldr M Liberal
11046 (07bree2:053) occupy wallstreet (th) (th) ABC 23 nonAdvDgreHldr F Liberal
11047 (07bree2:054) Occupy Wallstreet (th) (th) ABC 1 nonAdvDgreHldr F Moderate
11048 (07bree2:055) Occupy Wallstreet movement (th) (th) ABC 43 AdvDgreHldr F Liberal
11049 (07bree2:056) out of date (ab) (ab) ABC 36 nonAdvDgreHldr M Liberal
11050 (07bree2:057) peace (th) (th) ABC 4 AdvDgreHldr M Liberal
11051 (07bree2:058) playing on occupy wall street (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11052 (07bree2:058) playing on occupy wall street (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11053 (07bree2:062) pointless (ab) (ab) ABC 12 nonAdvDgreHldr F Moderate
11054 (07bree2:063) police (pe) (pe) PEO 4 AdvDgreHldr M Liberal
11055 (07bree2:063) police (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11056 (07bree2:064) Police (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
11057 (07bree2:064) Police (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11058 (07bree2:065) police (pe) (pe) PEO 38 nonAdvDgreHldr F Liberal
11059 (07bree2:065) police (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
11060 (07bree2:066) politicans are hipocrits (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11061 (07bree2:066) politicans are hipocrits (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11062 (07bree2:068) pothead (pe) (pe) PEO 1 nonAdvDgreHldr F Moderate
11063 (07bree2:068) pothead (ss) (ss) PRA 1 nonAdvDgreHldr F Moderate
11064 (07bree2:069) protest (ev) (ev) C_S 26 nonAdvDgreHldr M Conservative
11065 (07bree2:070) protester (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
11066 (07bree2:070) protester (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11067 (07bree2:070) protester (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11068 (07bree2:071) protestor (pe) (pe) PEO 4 AdvDgreHldr M Liberal
11069 (07bree2:071) protestor (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11070 (07bree2:071) protestor (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11071 (07bree2:072) Reforming policies (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11072 (07bree2:073) rights (th) (th) ABC 24 nonAdvDgreHldr F Moderate
11073 (07bree2:074) san diego (tx) (tx) LOB 15 nonAdvDgreHldr F Liberal
11074 (07bree2:075) San Diego Union-Tribune (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
11075 (07bree2:075) San Diego Union-Tribune (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11076 (07bree2:076) sick of this debate (pr) (pr) VRE 42 nonAdvDgreHldr M Moderate
276
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11077 (07bree2:077) smoke (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11078 (07bree2:078) smoking (ac) (ac) C_S 4 AdvDgreHldr M Liberal
11079 (07bree2:079) stabilize economy (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11080 (07bree2:080) stronghold (ab) (ab) ABC 40 nonAdvDgreHldr F Liberal
11081 (07bree2:081) the future (tr) (tr) AHI 24 nonAdvDgreHldr F Moderate
11082 (07bree2:082) the rules are changing (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
11083 (07bree2:083) they will never legalize it (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11084 (07bree2:084) this comic is over my head (un) (un) VRE 34 nonAdvDgreHldr M Moderate
11085 (07bree2:085) truth (ab) (ab) ABC 40 nonAdvDgreHldr F Liberal
11086 (07bree2:086) U.S. becoming more liberal (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11087 (07bree2:086) U.S. becoming more liberal (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11088 (07bree2:087) Unpreventable (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
11089 (07bree2:088) weed (ob) (ob) LOB 1 nonAdvDgreHldr F Moderate
11090 (07bree2:088) weed (th) (th) ABC 1 nonAdvDgreHldr F Moderate
11091 (07bree2:089) weed (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11092 (07bree2:089) weed (th) (th) ABC 4 AdvDgreHldr M Liberal
11093 (07bree2:090) weed (ob) (ob) LOB 15 nonAdvDgreHldr F Liberal
11094 (07bree2:090) weed (th) (th) ABC 15 nonAdvDgreHldr F Liberal
11095 (07bree2:091) weed (ob) (ob) LOB 26 nonAdvDgreHldr M Conservative
11096 (07bree2:091) weed (th) (th) ABC 26 nonAdvDgreHldr M Conservative
11097 (07bree2:092) weed (ob) (ob) LOB 28 nonAdvDgreHldr F Moderate
11098 (07bree2:092) weed (th) (th) ABC 28 nonAdvDgreHldr F Moderate
11099 (07bree2:093) weed going mainstream (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
11100 (07bree2:093) weed going mainstream (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11101 (07bree2:094) Weed will help stabilize the economy (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
11102 (07bree2:094) Weed will help stabilize the economy (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11103 (07bree2:096) who is Bob Filner (un) (un) VRE 34 nonAdvDgreHldr M Moderate
11104 (07bree2:900) Bob Filner (pe) (pe) PEO 38 nonAdvDgreHldr F Liberal
11105 (07bree2:900) Bob Filner (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
11106 (07bree2:901) counter culture (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11107 (07bree2:902) hilarious (ab) (ab) ABC 11 nonAdvDgreHldr M Liberal
11108 (07bree2:903) Hippy (pe) (pe) PEO 33 nonAdvDgreHldr F Moderate
11109 (07bree2:903) Hippy (ss) (ss) PRA 33 nonAdvDgreHldr F Moderate
11110 (07bree2:904) hippy (pe) (pe) PEO 40 nonAdvDgreHldr F Liberal
11111 (07bree2:904) hippy (ss) (ss) PRA 40 nonAdvDgreHldr F Liberal
277
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11112 (07bree2:905) is legalizing weed close? (cn) (cn) VRE 11 nonAdvDgreHldr M Liberal
11113 (07bree2:906) Legalize (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
11114 (07bree2:907) legalize (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11115 (07bree2:908) Little Differences (ab) (ab) ABC 23 nonAdvDgreHldr F Liberal
11116 (07bree2:909) marijuana (ob) (ob) LOB 25 nonAdvDgreHldr F Conservative
11117 (07bree2:909) marijuana (th) (th) ABC 25 nonAdvDgreHldr F Conservative
11118 (07bree2:910) marijuana (ob) (ob) LOB 27 nonAdvDgreHldr M Liberal
11119 (07bree2:910) marijuana (th) (th) ABC 27 nonAdvDgreHldr M Liberal
11120 (07bree2:911) Marijuana (ob) (ob) LOB 30 nonAdvDgreHldr M Conservative
11121 (07bree2:911) Marijuana (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11122 (07bree2:912) Marijuana (ob) (ob) LOB 43 AdvDgreHldr F Liberal
11123 (07bree2:912) Marijuana (th) (th) ABC 43 AdvDgreHldr F Liberal
11124 (07bree2:913) NormL (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
11125 (07bree2:914) Occupy Wallstreet (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11126 (07bree2:915) police ignorance (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
11127 (07bree2:915) police ignorance (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11128 (07bree2:916) There are worse things out there that are legal (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
11129 (07bree2:917) unsure (un) (un) VRE 13 nonAdvDgreHldr F Moderate
11130 (07bree2:918) weird (pr) (pr) VRE 5 nonAdvDgreHldr M Conservative
11131 (07bree2:919) west coast mentality (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11132 (07bree2:919) west coast mentality (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11133 (08hand2:001) America (ab) (ab) ABC 4 AdvDgreHldr M Liberal
11134 (08hand2:001) America (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11135 (08hand2:002) america (ab) (ab) ABC 25 nonAdvDgreHldr F Conservative
11136 (08hand2:002) america (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11137 (08hand2:003) America (ab) (ab) ABC 43 AdvDgreHldr F Liberal
11138 (08hand2:003) America (tx) (tx) LOB 43 AdvDgreHldr F Liberal
11139 (08hand2:004) America's fading middle class (ss) (ss) PRA 33 nonAdvDgreHldr F Moderate
11140 (08hand2:004) America's fading middle class (th) (th) ABC 33 nonAdvDgreHldr F Moderate
11141 (08hand2:004) America's fading middle class (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
11142 (08hand2:005) anti-bourgouise (at) (at) ABC 17 AdvDgreHldr F Moderate
11143 (08hand2:006) average man fading (pe) (pe) PEO 3 nonAdvDgreHldr F Conservative
11144 (08hand2:006) average man fading (ss) (ss) PRA 3 nonAdvDgreHldr F Conservative
11145 (08hand2:007) biggest problem (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
11146 (08hand2:008) Class separation (th) (th) ABC 43 AdvDgreHldr F Liberal
278
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11147 (08hand2:009) Class wars (th) (th) ABC 43 AdvDgreHldr F Liberal
11148 (08hand2:010) common knowledge (pr) (pr) VRE 8 nonAdvDgreHldr F Moderate
11149 (08hand2:011) disappear (ab) (ab) ABC 1 nonAdvDgreHldr F Moderate
11150 (08hand2:012) economy (th) (th) ABC 4 AdvDgreHldr M Liberal
11151 (08hand2:013) economy (th) (th) ABC 43 AdvDgreHldr F Liberal
11152 (08hand2:014) economy hurting people (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11153 (08hand2:014) economy hurting people (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11154 (08hand2:015) effective (ab) (ab) ABC 34 nonAdvDgreHldr M Moderate
11155 (08hand2:016) either rich or poor (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11156 (08hand2:017) extremes (ab) (ab) ABC 12 nonAdvDgreHldr F Moderate
11157 (08hand2:017) extremes (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11158 (08hand2:018) fadding man is middle class (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
11159 (08hand2:018) fadding man is middle class (ss) (ss) PRA 3 nonAdvDgreHldr F Conservative
11160 (08hand2:019) fade (ac) (ac) C_S 4 AdvDgreHldr M Liberal
11161 (08hand2:020) fading (ac) (ac) C_S 4 AdvDgreHldr M Liberal
11162 (08hand2:020) fading (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11163 (08hand2:021) Fading (ac) (ac) C_S 20 nonAdvDgreHldr M Moderate
11164 (08hand2:021) Fading (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
11165 (08hand2:022) Fading (ac) (ac) C_S 25 nonAdvDgreHldr F Conservative
11166 (08hand2:022) Fading (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11167 (08hand2:023) Fading (ac) (ac) C_S 32 nonAdvDgreHldr F Conservative
11168 (08hand2:023) Fading (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
11169 (08hand2:024) fading (ac) (ac) C_S 37 nonAdvDgreHldr M Moderate
11170 (08hand2:024) fading (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
11171 (08hand2:025) funnies (ca) (ca) C_S 26 nonAdvDgreHldr M Conservative
11172 (08hand2:026) funny (at) (at) ABC 5 nonAdvDgreHldr M Conservative
11173 (08hand2:027) gentrification (th) (th) ABC 17 AdvDgreHldr F Moderate
11174 (08hand2:028) Great Recession (rf) (rf) ERE 4 AdvDgreHldr M Liberal
11175 (08hand2:029) guy is literally fading (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11176 (08hand2:030) guy is middle class (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11177 (08hand2:031) guy looks middle class (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11178 (08hand2:032) Half filled, or half empty? (pr) (pr) VRE 39 nonAdvDgreHldr M Conservative
11179 (08hand2:033) Handelsman (ar) (ar) AHI 4 AdvDgreHldr M Liberal
11180 (08hand2:034) Income inequality (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11181 (08hand2:035) inevitable (at) (at) ABC 36 nonAdvDgreHldr M Liberal
279
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11182 (08hand2:036) invisible (th) (th) ABC 8 nonAdvDgreHldr F Moderate
11183 (08hand2:037) loss of jobs (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11184 (08hand2:038) majority (th) (th) ABC 40 nonAdvDgreHldr F Liberal
11185 (08hand2:039) Middle class (ss) (ss) PRA 1 nonAdvDgreHldr F Moderate
11186 (08hand2:039) Middle class (th) (th) ABC 1 nonAdvDgreHldr F Moderate
11187 (08hand2:039) Middle class (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
11188 (08hand2:040) middle class (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11189 (08hand2:040) middle class (th) (th) ABC 4 AdvDgreHldr M Liberal
11190 (08hand2:040) middle class (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11191 (08hand2:041) Middle class (ss) (ss) PRA 15 nonAdvDgreHldr F Liberal
11192 (08hand2:041) Middle class (th) (th) ABC 15 nonAdvDgreHldr F Liberal
11193 (08hand2:041) Middle class (tx) (tx) LOB 15 nonAdvDgreHldr F Liberal
11194 (08hand2:042) middle class (ss) (ss) PRA 21 nonAdvDgreHldr F Liberal
11195 (08hand2:042) middle class (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11196 (08hand2:042) middle class (tx) (tx) LOB 21 nonAdvDgreHldr F Liberal
11197 (08hand2:043) middle class (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11198 (08hand2:043) middle class (th) (th) ABC 25 nonAdvDgreHldr F Conservative
11199 (08hand2:043) middle class (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11200 (08hand2:044) Middle Class (ss) (ss) PRA 32 nonAdvDgreHldr F Conservative
11201 (08hand2:044) Middle Class (th) (th) ABC 32 nonAdvDgreHldr F Conservative
11202 (08hand2:044) Middle Class (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
11203 (08hand2:045) Middle class (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
11204 (08hand2:045) Middle class (th) (th) ABC 37 nonAdvDgreHldr M Moderate
11205 (08hand2:045) Middle class (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
11206 (08hand2:046) middle class america (th) (th) ABC 19 AdvDgreHldr F Moderate
11207 (08hand2:047) minority (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11209 (08hand2:048) more lower class (pr) (pr) VRE 42 nonAdvDgreHldr M Moderate
11208 (08hand2:048) more lower class (th) (th) ABC 42 nonAdvDgreHldr M Moderate
11210 (08hand2:049) NBA (WTF) (WTF) WTF 37 nonAdvDgreHldr M Moderate
11211 (08hand2:050) nebulous classes (th) (th) ABC 17 AdvDgreHldr F Moderate
11212 (08hand2:051) news (ab) (ab) ABC 1 nonAdvDgreHldr F Moderate
11213 (08hand2:052) news (ab) (ab) ABC 4 AdvDgreHldr M Liberal
11214 (08hand2:053) news (ab) (ab) ABC 25 nonAdvDgreHldr F Conservative
11215 (08hand2:054) Newsday (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
11216 (08hand2:055) newspaper (ob) (ob) LOB 4 AdvDgreHldr M Liberal
280
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11217 (08hand2:056) Newspaper Headlines (ob) (ob) LOB 38 nonAdvDgreHldr F Liberal
11218 (08hand2:057) no middle class (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
11219 (08hand2:057) no middle class (th) (th) ABC 2 nonAdvDgreHldr F Conservative
11220 (08hand2:058) only lower higher class (WTF) (WTF) WTF 2 nonAdvDgreHldr F Conservative
11221 (08hand2:059) political (at) (at) ABC 6 nonAdvDgreHldr M Conservative
11222 (08hand2:060) recession (th) (th) ABC 4 AdvDgreHldr M Liberal
11223 (08hand2:061) recession (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11224 (08hand2:062) Rich versus poor (pr) (pr) VRE 43 AdvDgreHldr F Liberal
11225 (08hand2:062) Rich versus poor (th) (th) ABC 43 AdvDgreHldr F Liberal
11226 (08hand2:063) share of income (th) (th) ABC 7 nonAdvDgreHldr M Moderate
11227 (08hand2:064) solutions (ab) (ab) ABC 24 nonAdvDgreHldr F Moderate
11228 (08hand2:065) subtle (ab) (ab) ABC 34 nonAdvDgreHldr M Moderate
11229 (08hand2:066) symbolism (sm) (sm) ABC 26 nonAdvDgreHldr M Conservative
11230 (08hand2:067) The disappearing act (pr) (pr) VRE 39 nonAdvDgreHldr M Conservative
11231 (08hand2:068) the future (pr) (pr) VRE 24 nonAdvDgreHldr F Moderate
11232 (08hand2:069) the middle class is decreasing (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
11233 (08hand2:069) the middle class is decreasing (th) (th) ABC 29 nonAdvDgreHldr F Conservative
11234 (08hand2:070) too late (pr) (pr) VRE 1 nonAdvDgreHldr F Moderate
11235 (08hand2:071) translucent (de) (de) DES 4 AdvDgreHldr M Liberal
11236 (08hand2:072) transparent (de) (de) DES 4 AdvDgreHldr M Liberal
11237 (08hand2:073) Underrepresented (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11238 (08hand2:074) unemployment (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11239 (08hand2:075) unfortunate (ab) (ab) ABC 13 nonAdvDgreHldr F Moderate
11240 (08hand2:075) unfortunate (pr) (pr) VRE 13 nonAdvDgreHldr F Moderate
11241 (08hand2:076) United States (rf) (rf) ERE 4 AdvDgreHldr M Liberal
11242 (08hand2:077) United States (rf) (rf) ERE 43 AdvDgreHldr F Liberal
11243 (08hand2:078) US (rf) (rf) ERE 4 AdvDgreHldr M Liberal
11244 (08hand2:080) very true (ab) (ab) ABC 36 nonAdvDgreHldr M Liberal
11245 (08hand2:081) well thought out [cartoon] (fo) (fo) AHI 34 nonAdvDgreHldr M Moderate
11246 (08hand2:081) well thought out cartoon (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11247 (08hand2:900) disappearing (ab) (ab) ABC 21 nonAdvDgreHldr F Liberal
11248 (08hand2:901) economic issue (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11249 (08hand2:902) eye catching (de) (de) DES 11 nonAdvDgreHldr M Liberal
11250 (08hand2:903) Fading (ac) (ac) C_S 38 nonAdvDgreHldr F Liberal
11251 (08hand2:903) Fading (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
281
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11252 (08hand2:904) Fading Middle Class (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11253 (08hand2:904) Fading Middle Class (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
11254 (08hand2:905) favorite cartoon thus far (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11255 (08hand2:906) He is fading (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
11256 (08hand2:907) He is part of the middle class (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
11257 (08hand2:908) ironic (ab) (ab) ABC 27 nonAdvDgreHldr M Liberal
11258 (08hand2:909) Middle Class (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
11259 (08hand2:909) Middle Class (th) (th) ABC 38 nonAdvDgreHldr F Liberal
11260 (08hand2:909) Middle Class (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
11261 (08hand2:910) Middle class (ss) (ss) PRA 43 AdvDgreHldr F Liberal
11262 (08hand2:910) Middle class (th) (th) ABC 43 AdvDgreHldr F Liberal
11263 (08hand2:910) Middle class (tx) (tx) LOB 43 AdvDgreHldr F Liberal
11264 (08hand2:911) News (ab) (ab) ABC 32 nonAdvDgreHldr F Conservative
11265 (08hand2:912) probably relates to many ppl (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11266 (08hand2:913) rich get richer (pr) (pr) VRE 27 nonAdvDgreHldr M Liberal
11267 (08hand2:914) sounds about right (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
11268 (08hand2:915) symbolic [cartoon] (fo) (fo) AHI 27 nonAdvDgreHldr M Liberal
11269 (08hand2:915) symbolic cartoon (sm) (sm) ABC 27 nonAdvDgreHldr M Liberal
11270 (08hand2:916) The 1% (rf) (rf) ERE 30 nonAdvDgreHldr M Conservative
11271 (08hand2:917) unfortunate (ab) (ab) ABC 36 nonAdvDgreHldr M Liberal
11272 (08hand2:917) unfortunate (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
11273 (08hand2:918) US economy (rf) (rf) ERE 4 AdvDgreHldr M Liberal
11274 (08hand2:919) wonderful (ab) (ab) ABC 11 nonAdvDgreHldr M Liberal
11275 (08hand2:919) wonderful (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11276 (09luck2:001) NBA (th) (th) ABC 4 AdvDgreHldr M Liberal
11277 (09luck2:001) NBA (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11278 (09luck2:002) americans shifting focus (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11279 (09luck2:002) americans shifting focus (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11280 (09luck2:003) Atlanta Journal-Constitution (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
11281 (09luck2:004) basketball (th) (th) ABC 2 nonAdvDgreHldr F Conservative
11282 (09luck2:005) basketball (th) (th) ABC 13 nonAdvDgreHldr F Moderate
11283 (09luck2:006) Basketball [Player] (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
11284 (09luck2:006) Basketball Player (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11285 (09luck2:007) Disagreement (ab) (ab) ABC 22 nonAdvDgreHldr F Moderate
11286 (09luck2:008) disagreements (ab) (ab) ABC 8 nonAdvDgreHldr F Moderate
282
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11287 (09luck2:009) Disregard (at) (at) ABC 22 nonAdvDgreHldr F Moderate
11288 (09luck2:010) distracted (at) (at) ABC 4 AdvDgreHldr M Liberal
11289 (09luck2:011) entertainment (th) (th) ABC 42 nonAdvDgreHldr M Moderate
11290 (09luck2:012) Entertainment television (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11291 (09luck2:013) fan protest (ac) (ac) ABC 1 nonAdvDgreHldr F Moderate
11292 (09luck2:014) fans (th) (th) ABC 4 AdvDgreHldr M Liberal
11293 (09luck2:015) fans will find something else (pr) (pr) VRE 26 nonAdvDgreHldr M Conservative
11294 (09luck2:015) fans will find something else (th) (th) ABC 26 nonAdvDgreHldr M Conservative
11295 (09luck2:016) fed up (at) (at) ABC 4 AdvDgreHldr M Liberal
11296 (09luck2:017) fickle fans (th) (th) ABC 19 AdvDgreHldr F Moderate
11297 (09luck2:018) frustrating (at) (at) ABC 36 nonAdvDgreHldr M Liberal
11298 (09luck2:019) funny (pr) (pr) VRE 5 nonAdvDgreHldr M Conservative
11299 (09luck2:020) funny (pr) (pr) VRE 24 nonAdvDgreHldr F Moderate
11300 (09luck2:021) greed (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11301 (09luck2:022) Greed (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11302 (09luck2:023) Hockey will gain more popularity (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11303 (09luck2:024) Hockey will gain more popularity (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11304 (09luck2:025) house (se) (se) C_S 4 AdvDgreHldr M Liberal
11305 (09luck2:026) hypocritical (at) (at) ABC 24 nonAdvDgreHldr F Moderate
11306 (09luck2:027) I miss basketball (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11307 (09luck2:028) lebron james (PEO) (PEO) PEO 15 nonAdvDgreHldr F Liberal
11308 (09luck2:029) lebron james (PEO) (PEO) PEO 36 nonAdvDgreHldr M Liberal
11309 (09luck2:030) less sports, more glam (pr) (pr) VRE 17 AdvDgreHldr F Moderate
11310 (09luck2:031) Lock out (th) (th) ABC 1 nonAdvDgreHldr F Moderate
11311 (09luck2:032) lock out (th) (th) ABC 23 nonAdvDgreHldr F Liberal
11312 (09luck2:033) locked out (ev) (ev) C_S 37 nonAdvDgreHldr M Moderate
11313 (09luck2:033) locked out (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
11314 (09luck2:034) Locked Out (ev) (ev) C_S 25 nonAdvDgreHldr F Conservative
11315 (09luck2:034) Locked Out (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11316 (09luck2:035) lockout (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11317 (09luck2:036) Lockout (th) (th) ABC 33 nonAdvDgreHldr F Moderate
11318 (09luck2:037) lockout (th) (th) ABC 38 nonAdvDgreHldr F Liberal
11319 (09luck2:038) Lockout (th) (th) ABC 43 AdvDgreHldr F Liberal
11320 (09luck2:039) lost revenue (th) (th) ABC 7 nonAdvDgreHldr M Moderate
11321 (09luck2:040) loves basketball (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
283
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11322 (09luck2:041) Mike Luckovich (ar) (ar) AHI 4 AdvDgreHldr M Liberal
11323 (09luck2:041) Mike Luckovich (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11324 (09luck2:042) money (th) (th) ABC 26 nonAdvDgreHldr M Conservative
11325 (09luck2:043) more harm then good (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
11326 (09luck2:044) my poor boyfriend (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11327 (09luck2:045) NBA (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11328 (09luck2:045) NBA (th) (th) ABC 25 nonAdvDgreHldr F Conservative
11329 (09luck2:045) NBA (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11330 (09luck2:046) NBA (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11331 (09luck2:046) NBA (th) (th) ABC 26 nonAdvDgreHldr M Conservative
11332 (09luck2:046) NBA (tx) (tx) LOB 26 nonAdvDgreHldr M Conservative
11333 (09luck2:047) NBA (ss) (ss) PRA 26 nonAdvDgreHldr M Conservative
11334 (09luck2:047) NBA (ss) (ss) PRA 30 nonAdvDgreHldr M Conservative
11335 (09luck2:047) NBA (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11336 (09luck2:047) NBA (tx) (tx) LOB 30 nonAdvDgreHldr M Conservative
11337 (09luck2:048) nba (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
11338 (09luck2:048) nba (th) (th) ABC 38 nonAdvDgreHldr F Liberal
11339 (09luck2:048) nba (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
11340 (09luck2:049) NBA Fan (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
11341 (09luck2:050) NBA lockout (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11342 (09luck2:051) NBA Lockout (th) (th) ABC 7 nonAdvDgreHldr M Moderate
11343 (09luck2:052) NBA lockout (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11344 (09luck2:053) NBA Lockout (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11345 (09luck2:054) NBA Lockout (th) (th) ABC 34 nonAdvDgreHldr M Moderate
11346 (09luck2:055) NBA lockout-2011 (ta) (ta) C_S 19 AdvDgreHldr F Moderate
11347 (09luck2:056) negotiations (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11348 (09luck2:057) Not even NBA fans care (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11349 (09luck2:057) Not even NBA fans care (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11350 (09luck2:058) Owner (pe) (pe) PEO 25 nonAdvDgreHldr F Conservative
11351 (09luck2:058) Owner (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11352 (09luck2:058) Owner (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11353 (09luck2:059) owner is white (pr) (pr) VRE 2 nonAdvDgreHldr F Conservative
11354 (09luck2:060) owners (PEO) (PEO) PEO 4 AdvDgreHldr M Liberal
11355 (09luck2:060) owners (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11356 (09luck2:061) owners (PEO) (PEO) PEO 7 nonAdvDgreHldr M Moderate
284
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11357 (09luck2:061) owners (ss) (ss) PRA 7 nonAdvDgreHldr M Moderate
11358 (09luck2:062) owners and players (PEO) (PEO) PEO 1 nonAdvDgreHldr F Moderate
11359 (09luck2:062) owners and players (ss) (ss) PRA 1 nonAdvDgreHldr F Moderate
11360 (09luck2:063) player's union (ss) (ss) PRA 7 nonAdvDgreHldr M Moderate
11361 (09luck2:063) player's union (th) (th) ABC 7 nonAdvDgreHldr M Moderate
11362 (09luck2:065) players (PEO) (PEO) PEO 4 AdvDgreHldr M Liberal
11363 (09luck2:065) players (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11364 (09luck2:065) players (th) (th) ABC 4 AdvDgreHldr M Liberal
11365 (09luck2:067) Real Housewives of Atlanta (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11366 (09luck2:068) reality t.v. potato (pe) (pe) PEO 17 AdvDgreHldr F Moderate
11367 (09luck2:068) reality t.v. potato (ss) (ss) PRA 17 AdvDgreHldr F Moderate
11368 (09luck2:069) Reality TV (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11369 (09luck2:070) sad (at) (at) ABC 12 nonAdvDgreHldr F Moderate
11370 (09luck2:071) Sports (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11371 (09luck2:072) sports (th) (th) ABC 34 nonAdvDgreHldr M Moderate
11372 (09luck2:073) sports drama (th) (th) ABC 8 nonAdvDgreHldr F Moderate
11373 (09luck2:074) Standards have changed (pr) (pr) VRE 29 nonAdvDgreHldr F Conservative
11374 (09luck2:075) stereotypes (ab) (ab) ABC 40 nonAdvDgreHldr F Liberal
11375 (09luck2:076) surprise visit (ev) (ev) C_S 2 nonAdvDgreHldr F Conservative
11376 (09luck2:078) too much reality tv (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11377 (09luck2:078) too much reality tv (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11378 (09luck2:079) TV (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11379 (09luck2:080) unfair (at) (at) ABC 36 nonAdvDgreHldr M Liberal
11380 (09luck2:081) unrealistic (at) (at) ABC 17 AdvDgreHldr F Moderate
11381 (09luck2:082) versus (WTF) (WTF) WTF 24 nonAdvDgreHldr F Moderate
11382 (09luck2:083) white and black (pr) (pr) VRE 1 nonAdvDgreHldr F Moderate
11383 (09luck2:900) basketball (th) (th) ABC 15 nonAdvDgreHldr F Liberal
11384 (09luck2:901) bravo network (rf) (rf) ERE 13 nonAdvDgreHldr F Moderate
11385 (09luck2:902) fans (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11386 (09luck2:903) fans not affected (pr) (pr) VRE 21 nonAdvDgreHldr F Liberal
11387 (09luck2:903) fans not affected (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11388 (09luck2:904) greed (th) (th) ABC 40 nonAdvDgreHldr F Liberal
11389 (09luck2:905) kris humphries (pe) (pe) PEO 13 nonAdvDgreHldr F Moderate
11390 (09luck2:906) Lebron? (cn) (cn) VRE 27 nonAdvDgreHldr M Liberal
11391 (09luck2:907) losin faith in deal being done (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
285
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11392 (09luck2:907) losin faith in deal being done (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11393 (09luck2:908) money (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11394 (09luck2:909) NBA (ss) (ss) PRA 43 AdvDgreHldr F Liberal
11395 (09luck2:909) NBA (th) (th) ABC 43 AdvDgreHldr F Liberal
11396 (09luck2:909) NBA (tx) (tx) LOB 43 AdvDgreHldr F Liberal
11398 (09luck2:910) NBA Fan (pe) (pe) PEO 38 nonAdvDgreHldr F Liberal
11397 (09luck2:910) NBA Fan (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
11399 (09luck2:911) NBA Lockout (th) (th) ABC 39 nonAdvDgreHldr M Conservative
11400 (09luck2:912) No one cares (pr) (pr) VRE 23 nonAdvDgreHldr F Liberal
11401 (09luck2:912) No one cares (th) (th) ABC 23 nonAdvDgreHldr F Liberal
11402 (09luck2:913) people want to be entertained (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11403 (09luck2:913) people want to be entertained (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11404 (09luck2:914) people are losing interest (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11405 (09luck2:914) people are losing interest (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11406 (09luck2:915) player (pe) (pe) PEO 32 nonAdvDgreHldr F Conservative
11407 (09luck2:916) players and owners should be on the same side (pr) (pr) VRE 11 nonAdvDgreHldr M Liberal
11408 (09luck2:917) reality tv (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11409 (09luck2:918) the nba lockout (th) (th) ABC 3 nonAdvDgreHldr F Conservative
11410 (09luck2:919) were locked out (pr) (pr) VRE 32 nonAdvDgreHldr F Conservative
11411 (09luck2:920) You're nothing without fans (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
11412 (10rami2:001) :( (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11413 (10rami2:002) 1991 (WTF) (WTF) WTF 4 AdvDgreHldr M Liberal
11414 (10rami2:003) accepting of liberals (at) (at) ABC 14 nonAdvDgreHldr F Moderate
11415 (10rami2:003) accepting of liberals (at) (at) ABC 14 nonAdvDgreHldr F Moderate
11416 (10rami2:004) black (ss) (ss) PRA 15 nonAdvDgreHldr F Liberal
11417 (10rami2:005) Blacks (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11418 (10rami2:005) Blacks (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11419 (10rami2:006) blacks (ss) (ss) PRA 20 nonAdvDgreHldr M Moderate
11420 (10rami2:006) blacks (tx) (tx) LOB 20 nonAdvDgreHldr M Moderate
11421 (10rami2:007) Blacks (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11422 (10rami2:007) Blacks (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11423 (10rami2:008) Blacks (ss) (ss) PRA 32 nonAdvDgreHldr F Conservative
11424 (10rami2:008) Blacks (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
11425 (10rami2:009) Blacks (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
11426 (10rami2:009) Blacks (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
286
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11427 (10rami2:010) blacks are more conservative (pr) (pr) VRE 3 nonAdvDgreHldr F Conservative
11428 (10rami2:010) blacks are more conservative (th) (th) ABC 3 nonAdvDgreHldr F Conservative
11429 (10rami2:011) Colored (rf) (rf) ERE 4 AdvDgreHldr M Liberal
11430 (10rami2:012) conservative (ss) (ss) PRA 1 nonAdvDgreHldr F Moderate
11431 (10rami2:012) conservative (tx) (tx) LOB 1 nonAdvDgreHldr F Moderate
11432 (10rami2:013) Conservative (ss) (ss) PRA 4 AdvDgreHldr M Liberal
11433 (10rami2:013) Conservative (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11434 (10rami2:014) conservative (ss) (ss) PRA 25 nonAdvDgreHldr F Conservative
11435 (10rami2:014) conservative (tx) (tx) LOB 25 nonAdvDgreHldr F Conservative
11436 (10rami2:015) conservative (ss) (ss) PRA 32 nonAdvDgreHldr F Conservative
11437 (10rami2:015) conservative (tx) (tx) LOB 32 nonAdvDgreHldr F Conservative
11438 (10rami2:016) conservative blacks (ss) (ss) PRA 33 nonAdvDgreHldr F Moderate
11439 (10rami2:016) conservative blacks (th) (th) ABC 33 nonAdvDgreHldr F Moderate
11440 (10rami2:016) conservative blacks (tx) (tx) LOB 33 nonAdvDgreHldr F Moderate
11441 (10rami2:017) Conservative Blacks (ss) (ss) PRA 38 nonAdvDgreHldr F Liberal
11442 (10rami2:017) Conservative Blacks (th) (th) ABC 38 nonAdvDgreHldr F Liberal
11443 (10rami2:017) Conservative Blacks (tx) (tx) LOB 38 nonAdvDgreHldr F Liberal
11444 (10rami2:018) cool drawing style (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11445 (10rami2:018) cool drawing style (tc) (tc) AHI 34 nonAdvDgreHldr M Moderate
11446 (10rami2:020) Discrimination (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11447 (10rami2:021) double standard (th) (th) ABC 7 nonAdvDgreHldr M Moderate
11448 (10rami2:022) funny (at) (at) ABC 26 nonAdvDgreHldr M Conservative
11449 (10rami2:023) Herman Cain (PEO) (PEO) PEO 23 nonAdvDgreHldr F Liberal
11450 (10rami2:024) Herman Cain (PEO) (PEO) PEO 7 nonAdvDgreHldr M Moderate
11520 (10rami2:025) i hate racism (pr) (pr) VRE 12 nonAdvDgreHldr F Moderate
11451 (10rami2:026) Investors Business Daily (AHI) (AHI) AHI 4 AdvDgreHldr M Liberal
11452 (10rami2:026) Investors Business Daily (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11453 (10rami2:027) isolation of conservatives (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11454 (10rami2:027) isolation of conservatives (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11455 (10rami2:028) lack of minority conservatives (pr) (pr) VRE 43 AdvDgreHldr F Liberal
11456 (10rami2:028) lack of minority conservatives (th) (th) ABC 43 AdvDgreHldr F Liberal
11457 (10rami2:029) liberal add (WTF) (WTF) WTF 6 nonAdvDgreHldr M Conservative
11458 (10rami2:030) liberal [cartoon] (fo) (fo) AHI 6 nonAdvDgreHldr M Conservative
11459 (10rami2:030) liberal cartoon (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11460 (10rami2:032) might have hint of truth attac (un) (un) VRE 34 nonAdvDgreHldr M Moderate
287
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11461 (10rami2:033) Minority (ss) (ss) PRA 22 nonAdvDgreHldr F Moderate
11462 (10rami2:034) new minority (pr) (pr) VRE 10 nonAdvDgreHldr M Moderate
11463 (10rami2:034) new minority (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11464 (10rami2:035) not equal (ab) (ab) ABC 1 nonAdvDgreHldr F Moderate
11465 (10rami2:036) not representative (ab) (ab) ABC 36 nonAdvDgreHldr M Liberal
11466 (10rami2:037) not that funny (pr) (pr) VRE 34 nonAdvDgreHldr M Moderate
11467 (10rami2:038) obama as hypocrit (PEO) (PEO) PEO 14 nonAdvDgreHldr F Moderate
11468 (10rami2:038) obama as hypocrit (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11469 (10rami2:039) obtuse (at) (at) ABC 4 AdvDgreHldr M Liberal
11470 (10rami2:040) old (ab) (ab) ABC 24 nonAdvDgreHldr F Moderate
11471 (10rami2:041) Painful (at) (at) ABC 39 nonAdvDgreHldr M Conservative
11472 (10rami2:042) party affiliations (th) (th) ABC 10 nonAdvDgreHldr M Moderate
11473 (10rami2:043) political (ab) (ab) ABC 2 nonAdvDgreHldr F Conservative
11474 (10rami2:044) prejudice (th) (th) ABC 4 AdvDgreHldr M Liberal
11475 (10rami2:045) Race (th) (th) ABC 22 nonAdvDgreHldr F Moderate
11476 (10rami2:046) racism (th) (th) ABC 2 nonAdvDgreHldr F Conservative
11477 (10rami2:047) racist (at) (at) ABC 19 AdvDgreHldr F Moderate
11478 (10rami2:048) racist (at) (at) ABC 15 nonAdvDgreHldr F Liberal
11479 (10rami2:049) racist (at) (at) ABC 8 nonAdvDgreHldr F Moderate
11480 (10rami2:050) racist (at) (at) ABC 34 nonAdvDgreHldr M Moderate
11481 (10rami2:051) racist (at) (at) ABC 6 nonAdvDgreHldr M Conservative
11482 (10rami2:052) racist (at) (at) ABC 29 nonAdvDgreHldr F Conservative
11483 (10rami2:053) racist (at) (at) ABC 42 nonAdvDgreHldr M Moderate
11484 (10rami2:054) Ramirez (ar) (ar) AHI 4 AdvDgreHldr M Liberal
11485 (10rami2:054) Ramirez (tx) (tx) LOB 4 AdvDgreHldr M Liberal
11486 (10rami2:055) Republican minorities (ss) (ss) PRA 43 AdvDgreHldr F Liberal
11487 (10rami2:055) Republican minorities (th) (th) ABC 43 AdvDgreHldr F Liberal
11488 (10rami2:056) says republicans are racist (pr) (pr) VRE 6 nonAdvDgreHldr M Conservative
11489 (10rami2:056) says republicans are racist (th) (th) ABC 6 nonAdvDgreHldr M Conservative
11490 (10rami2:057) segregation (rf) (rf) ERE 4 AdvDgreHldr M Liberal
11491 (10rami2:058) segregation (rf) (rf) ERE 19 AdvDgreHldr F Moderate
11492 (10rami2:059) Segregation (rf) (rf) ERE 17 AdvDgreHldr F Moderate
11493 (10rami2:060) segregation (rf) (rf) ERE 38 nonAdvDgreHldr F Liberal
11494 (10rami2:061) segregation (rf) (rf) ERE 40 nonAdvDgreHldr F Liberal
11495 (10rami2:062) segregation (rf) (rf) ERE 1 nonAdvDgreHldr F Moderate
288
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11496 (10rami2:063) segregation (rf) (rf) ERE 22 nonAdvDgreHldr F Moderate
11497 (10rami2:064) segregation (rf) (rf) ERE 24 nonAdvDgreHldr F Moderate
11498 (10rami2:065) segregation (rf) (rf) ERE 26 nonAdvDgreHldr M Conservative
11499 (10rami2:066) segregation in politics (pr) (pr) VRE 43 AdvDgreHldr F Liberal
11500 (10rami2:066) segregation in politics (th) (th) ABC 43 AdvDgreHldr F Liberal
11501 (10rami2:067) sensitive topic (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
11502 (10rami2:068) separation (rf) (rf) ERE 25 nonAdvDgreHldr F Conservative
11503 (10rami2:069) seperate (rf) (rf) ERE 1 nonAdvDgreHldr F Moderate
11504 (10rami2:070) short (ab) (ab) ABC 15 nonAdvDgreHldr F Liberal
11505 (10rami2:071) sign (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11506 (10rami2:072) sinks (ob) (ob) LOB 4 AdvDgreHldr M Liberal
11507 (10rami2:073) smaller (de) (de) DES 40 nonAdvDgreHldr F Liberal
11508 (10rami2:074) strayed from old values (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11509 (10rami2:074) strayed from old values (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11510 (10rami2:075) targeting obama (PEO) (PEO) PEO 14 nonAdvDgreHldr F Moderate
11511 (10rami2:076) too far (pr) (pr) VRE 5 nonAdvDgreHldr M Conservative
11512 (10rami2:077) too soon (pr) (pr) VRE 36 nonAdvDgreHldr M Liberal
11513 (10rami2:078) unaccepting of conservatives (pr) (pr) VRE 14 nonAdvDgreHldr F Moderate
11514 (10rami2:078) unaccepting of conservatives (th) (th) ABC 14 nonAdvDgreHldr F Moderate
11515 (10rami2:079) upper class (ab) (ab) ABC 42 nonAdvDgreHldr M Moderate
11516 (10rami2:080) washroom (se) (se) C_S 4 AdvDgreHldr M Liberal
11517 (10rami2:081) water (WTF) (WTF) WTF 26 nonAdvDgreHldr M Conservative
11518 (10rami2:082) water fountain (ob) (ob) LOB 25 nonAdvDgreHldr F Conservative
11519 (10rami2:083) water fountain (ob) (ob) LOB 38 nonAdvDgreHldr F Liberal
11521 (10rami2:900) Blacks (ss) (ss) PRA 43 AdvDgreHldr F Liberal
11522 (10rami2:900) Blacks (tx) (tx) LOB 43 AdvDgreHldr F Liberal
11523 (10rami2:901) conservation (WTF) (WTF) WTF 21 nonAdvDgreHldr F Liberal
11524 (10rami2:902) Conservative (ss) (ss) PRA 37 nonAdvDgreHldr M Moderate
11525 (10rami2:902) Conservative (tx) (tx) LOB 37 nonAdvDgreHldr M Moderate
11526 (10rami2:903) dirty (de) (de) DES 2 nonAdvDgreHldr F Conservative
11527 (10rami2:903) dirty (se) (se) C_S 2 nonAdvDgreHldr F Conservative
11528 (10rami2:904) Equality (th) (th) ABC 27 nonAdvDgreHldr M Liberal
11529 (10rami2:905) Herman Cain (PEO) (PEO) PEO 34 nonAdvDgreHldr M Moderate
11530 (10rami2:906) Herman Cain (PEO) (PEO) PEO 30 nonAdvDgreHldr M Conservative
11531 (10rami2:907) Obama (PEO) (PEO) PEO 30 nonAdvDgreHldr M Conservative
289
Table 33 - cotninued
PK term attrib Class p_id edu_type gen politics
11532 (10rami2:908) Progression (th) (th) ABC 27 nonAdvDgreHldr M Liberal
11533 (10rami2:910) race issue (th) (th) ABC 21 nonAdvDgreHldr F Liberal
11534 (10rami2:911) racial (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11535 (10rami2:912) Racial Segregation (th) (th) ABC 23 nonAdvDgreHldr F Liberal
11536 (10rami2:913) Racism (th) (th) ABC 27 nonAdvDgreHldr M Liberal
11537 (10rami2:914) Racism (th) (th) ABC 30 nonAdvDgreHldr M Conservative
11538 (10rami2:915) segregation (rf) (rf) ERE 30 nonAdvDgreHldr M Conservative
11539 (10rami2:916) segregation (rf) (rf) ERE 13 nonAdvDgreHldr F Moderate
11540 (10rami2:917) seperation within parties (th) (th) ABC 11 nonAdvDgreHldr M Liberal
11541 (10rami2:918) Two fountains are better than one (pr) (pr) VRE 31 nonAdvDgreHldr F Conservative
290
APPENDIX J
RAW QUERY ACTIVITY DATA
This data is in nine-point font to accommodate the size of the table, which in turn promotes the readability of the data. It was
felt that keeping the data for each tag was more important than the strict interpretation of APA formatting rules.
Table 34
Data from query activity PK terms attrib Class p id edu type gen politics
20001 (11ande1:001) [Anti-Obama Campaign] Fail (ab) (ab) ABC 100 nonDgreHldr F Moderate
20002 (11ande1:001) Anti-Obama Campaign Fail (at) (at) ABC 100 nonDgreHldr F Moderate
20003 (11ande1:001) Anti-Obama Campaign Fail (pr) (pr) VRE 100 nonDgreHldr F Moderate
20004 (11ande1:002) cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20005 (11ande1:003) current GOP criticism (pr) (pr) VRE 111 nonDgreHldr F Liberal
20006 (11ande1:003) current GOP criticism (th) (th) ABC 111 nonDgreHldr F Liberal
20007 (11ande1:004) Cutthroat Partisanship (at) (at) ABC 115 nonDgreHldr M Liberal
20008 (11ande1:004) Cutthroat Partisanship (pr) (pr) VRE 115 nonDgreHldr M Liberal
20009 (11ande1:005) democrat (ss) (ss) PRA 113 nonDgreHldr F Moderate
20010 (11ande1:006) democrats (ss) (ss) PRA 124 dgreHldr F Liberal
20011 (11ande1:007) fight (WTF) (WTF) WTF 116 nonDgreHldr F Conservative
20012 (11ande1:008) Fly Zone (ab) (ab) ABC 114 dgreHldr F Conservative
20013 (11ande1:008) Fly Zone (tx) (tx) LOB 114 dgreHldr F Conservative
20014 (11ande1:009) foreign policy (th) (th) ABC 119 nonDgreHldr M Moderate
20015 (11ande1:009) foreign policy (tx) (tx) LOB 119 nonDgreHldr M Moderate
20016 (11ande1:010) foreign policy (th) (th) ABC 121 dgreHldr M Moderate
20017 (11ande1:010) foreign policy (tx) (tx) LOB 121 dgreHldr M Moderate
20018 (11ande1:011) Foreign Policy (th) (th) ABC 114 dgreHldr F Conservative
20019 (11ande1:011) Foreign Policy (tx) (tx) LOB 114 dgreHldr F Conservative
20020 (11ande1:012) foreign policy (th) (th) ABC 124 dgreHldr F Liberal
20021 (11ande1:012) foreign policy (tx) (tx) LOB 124 dgreHldr F Liberal
20022 (11ande1:013) foreign policy (th) (th) ABC 109 dgreHldr M Moderate
20023 (11ande1:013) foreign policy (tx) (tx) LOB 109 dgreHldr M Moderate
20024 (11ande1:014) foreign policy issues (th) (th) ABC 117 nonDgreHldr F Conservative
20025 (11ande1:015) [Foreign policy] plane crash (th) (th) ABC 122 dgreHldr F Liberal
20026 (11ande1:015) [Foreign policy] plane crash (tx) (tx) LOB 122 dgreHldr F Liberal
20027 (11ande1:015) Foreign policy [plane crash] (se) (se) C/S 122 dgreHldr F Liberal
20028 (11ande1:016) GOP (ss) (ss) PRA 121 dgreHldr M Moderate
291
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20029 (11ande1:016) GOP (tx) (tx) LOB 121 dgreHldr M Moderate
20030 (11ande1:017) gop attacks on [obama] (PEO) (PEO) PEO 105 dgreHldr M Liberal
20031 (11ande1:017) gop attacks on obama (pr) (pr) VRE 105 dgreHldr M Liberal
20032 (11ande1:017) gop attacks on obama (th) (th) ABC 105 dgreHldr M Liberal
20033 (11ande1:018) [GOP plane] crash (de) (de) DES 104 nonDgreHldr M Moderate
20034 (11ande1:018) [GOP plane] crash (ob) (ob) LOB 104 nonDgreHldr M Moderate
20035 (11ande1:018) GOP [plane crash] (se) (se) C/S 104 nonDgreHldr M Moderate
20036 (11ande1:019) [gop plane] crash (de) (de) DES 123 nonDgreHldr M Moderate
20037 (11ande1:019) [gop plane] crash (ob) (ob) LOB 123 nonDgreHldr M Moderate
20038 (11ande1:019) gop [plane crash] (se) (se) C/S 123 nonDgreHldr M Moderate
20039 (11ande1:020) [gop] republican (ss) (ss) PRA 101 nonDgreHldr F Conservative
20040 (11ande1:020) gop [republican] (ss) (ss) PRA 101 nonDgreHldr F Conservative
20041 (11ande1:021) liberal (at) (at) ABC 113 nonDgreHldr F Moderate
20042 (11ande1:022) negativity on obamas foreign policy (at) (at) ABC 116 nonDgreHldr F Conservative
20043 (11ande1:022) negativity on obamas foreign policy (pr) (pr) VRE 116 nonDgreHldr F Conservative
20044 (11ande1:023) no fly zone (rf) (rf) ERE 118 nonDgreHldr F Conservative
20045 (11ande1:023) no fly zone (tx) (tx) LOB 118 nonDgreHldr F Conservative
20046 (11ande1:024) no fly zone (rf) (rf) ERE 102 dgreHldr M Liberal
20047 (11ande1:024) no fly zone (tx) (tx) LOB 102 dgreHldr M Liberal
20048 (11ande1:025) no fly zone (rf) (rf) ERE 120 dgreHldr F Moderate
20049 (11ande1:025) no fly zone (tx) (tx) LOB 120 dgreHldr F Moderate
20050 (11ande1:026) obama (PEO) (PEO) PEO 119 nonDgreHldr M Moderate
20051 (11ande1:027) obama (PEO) (PEO) PEO 113 nonDgreHldr F Moderate
20052 (11ande1:028) Obama (PEO) (PEO) PEO 117 nonDgreHldr F Conservative
20053 (11ande1:029) Obama (PEO) (PEO) PEO 114 dgreHldr F Conservative
20054 (11ande1:030) Obama (PEO) (PEO) PEO 124 dgreHldr F Liberal
20055 (11ande1:031) obama (PEO) (PEO) PEO 109 dgreHldr M Moderate
20056 (11ande1:032) obama foreign policy (th) (th) ABC 123 nonDgreHldr M Moderate
20057 (11ande1:033) obama foreign policy (th) (th) ABC 102 dgreHldr M Liberal
20058 (11ande1:034) Obama foreign policy (th) (th) ABC 120 dgreHldr F Moderate
20059 (11ande1:035) [Obama foreign policy failure] political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20060 (11ande1:035) [Obama foreign policy failure] political cartoon (at) (at) ABC 112 nonDgreHldr F Moderate
20061 (11ande1:035) Obama foreign policy failure political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20062 (11ande1:036) obama jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20063 (11ande1:037) obama lack of experience in foreign policy (th) (th) ABC 105 dgreHldr M Liberal
20064 (11ande1:038) [obama policy] cartoon spoof (th) (th) ABC 107 nonDgreHldr F Moderate
20065 (11ande1:038) obama policy [cartoon] spoof (fo) (fo) AHI 107 nonDgreHldr F Moderate
20066 (11ande1:038) obama policy cartoon spoof (ca) (ca) C/S 107 nonDgreHldr F Moderate
292
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20067 (11ande1:039) Obama (PEO) (PEO) PEO 100 nonDgreHldr F Moderate
20068 (11ande1:040) Bush vs. Obama (WTF) (WTF) WTF 100 nonDgreHldr F Moderate
20069 (11ande1:041) obamas foreign policy (th) (th) ABC 111 nonDgreHldr F Liberal
20070 (11ande1:041) obamas foreign policy (tx) (tx) LOB 111 nonDgreHldr F Liberal
20071 (11ande1:042) Obamas foreign policy is weak (th) (th) ABC 118 nonDgreHldr F Conservative
20072 (11ande1:042) Obamas foreign policy is weak (tx) (tx) LOB 118 nonDgreHldr F Conservative
20073 (11ande1:043) obama's foreign policy is weak (th) (th) ABC 101 nonDgreHldr F Conservative
20074 (11ande1:043) obama's foreign policy is weak (tx) (tx) LOB 101 nonDgreHldr F Conservative
20075 (11ande1:044) [obamas foreign policy is wea{k}] cartoon (th) (th) ABC 110 nonDgreHldr M Moderate
20076 (11ande1:044) [obamas foreign policy is wea{k}] cartoon (tx) (tx) LOB 110 nonDgreHldr M Moderate
20077 (11ande1:044) obamas foreign policy is wea{k} cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20078 (11ande1:045) politcal cartoon (fo) (fo) AHI 119 nonDgreHldr M Moderate
20079 (11ande1:046) political party arguments (pr) (pr) VRE 108 nonDgreHldr F Moderate
20080 (11ande1:046) political party arguments (th) (th) ABC 108 nonDgreHldr F Moderate
20081 (11ande1:047) President Obama (PEO) (PEO) PEO 121 dgreHldr M Moderate
20082 (11ande1:048) republican humor (ca) (ca) C/S 119 nonDgreHldr M Moderate
20083 (11ande1:049) republican opinions of obama's decisions (pr) (pr) VRE 108 nonDgreHldr F Moderate
20084 (11ande1:049) republican opinions of obama's decisions (th) (th) ABC 108 nonDgreHldr F Moderate
20085 (11ande1:050) republican party cartoons (fo) (fo) AHI 117 nonDgreHldr F Conservative
20086 (11ande1:050) republican party cartoons (ss) (ss) PRA 117 nonDgreHldr F Conservative
20087 (11ande1:051) republicans (ss) (ss) PRA 109 dgreHldr M Moderate
20088 (11ande1:052) republicians (ss) (ss) PRA 118 nonDgreHldr F Conservative
20089 (11ande1:053) the wreck of obama's foreign policy [plane] (ob) (ob) LOB 103 nonDgreHldr F Moderate
20090 (11ande1:053) the wreck of obama's foreign policy plane (pr) (pr) VRE 103 nonDgreHldr F Moderate
20091 (11ande1:053) the wreck of obama's foreign policy plane (se) (se) C/S 103 nonDgreHldr F Moderate
20092 (11ande1:054) weak (at) (at) ABC 114 dgreHldr F Conservative
20093 (11ande1:055) [weak foreign policy] with plane cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20094 (11ande1:055) weak foreign policy with [plane] cartoon (ob) (ob) LOB 106 nonDgreHldr F Moderate
20095 (11ande1:055) weak foreign policy with plane [cartoon] (fo) (fo) AHI 106 nonDgreHldr F Moderate
20096 (12bree1:001) animals on strike (ob) (ob) LOB 101 nonDgreHldr F Conservative
20097 (12bree1:002) corny occupoy wall street parody (ca) (ca) C/S 105 dgreHldr M Liberal
20098 (12bree1:002) corny occupoy wall street parody (pr) (pr) VRE 105 dgreHldr M Liberal
20099 (12bree1:003) dolphin slayings (ab) (ab) ABC 113 nonDgreHldr F Moderate
20100 (12bree1:004) dolphins (ob) (ob) LOB 124 dgreHldr F Liberal
20101 (12bree1:004) dolphins (ob) (ob) LOB 109 dgreHldr M Moderate
20102 (12bree1:005) [dolphins] and peta cartoon (ob) (ob) LOB 123 nonDgreHldr M Moderate
20103 (12bree1:005) dolphins and [peta] cartoon (ss) (ss) PRA 123 nonDgreHldr M Moderate
20104 (12bree1:005) dolphins and peta cartoon (fo) (fo) AHI 123 nonDgreHldr M Moderate
293
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20105 (12bree1:006) extreme peta people (PEO) (PEO) PEO 105 dgreHldr M Liberal
20106 (12bree1:006) extreme peta people (ss) (ss) PRA 105 dgreHldr M Liberal
20107 (12bree1:007) Free (tx) (tx) LOB 114 dgreHldr F Conservative
20108 (12bree1:008) free shamu (tx) (tx) LOB 123 nonDgreHldr M Moderate
20109 (12bree1:009) free the animals jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20110 (12bree1:009) free the animals jokes (pr) (pr) VRE 105 dgreHldr M Liberal
20111 (12bree1:010) [hippie guy] trying to save the animals (pe) (pe) PEO 105 dgreHldr M Liberal
20112 (12bree1:010) [hippie guy] trying to save the animals (ss) (ss) PRA 105 dgreHldr M Liberal
20113 (12bree1:010) hippie guy trying to save the animals (pr) (pr) VRE 105 dgreHldr M Liberal
20114 (12bree1:010) hippie guy trying to save the animals (th) (th) ABC 105 dgreHldr M Liberal
20115 (12bree1:011) how many occupy's are there now? (pr) (pr) VRE 108 nonDgreHldr F Moderate
20116 (12bree1:012) marine life (ob) (ob) LOB 113 nonDgreHldr F Moderate
20117 (12bree1:013) middle class (WTF) (WTF) WTF 100 nonDgreHldr F Moderate
20118 (12bree1:014) modern day movements (th) (th) ABC 103 nonDgreHldr F Moderate
20119 (12bree1:015) occupy (tx) (tx) LOB 109 dgreHldr M Moderate
20120 (12bree1:016) Occupy (tx) (tx) LOB 100 nonDgreHldr F Moderate
20121 (12bree1:017) Occupy (tx) (tx) LOB 121 dgreHldr M Moderate
20122 (12bree1:018) occupy movement (th) (th) ABC 111 nonDgreHldr F Liberal
20123 (12bree1:019) Occupy Movement (th) (th) ABC 115 nonDgreHldr M Liberal
20124 (12bree1:020) Occupy movement (th) (th) ABC 120 dgreHldr F Moderate
20125 (12bree1:021) [occupy movement] spoof (th) (th) ABC 104 nonDgreHldr M Moderate
20126 (12bree1:021) occupy movement spoof (ca) (ca) C/S 104 nonDgreHldr M Moderate
20127 (12bree1:022) occupy sea worl (th) (th) ABC 123 nonDgreHldr M Moderate
20128 (12bree1:022) occupy sea worl (tx) (tx) LOB 123 nonDgreHldr M Moderate
20129 (12bree1:023) occupy sea world (th) (th) ABC 118 nonDgreHldr F Conservative
20130 (12bree1:023) occupy sea world (tx) (tx) LOB 118 nonDgreHldr F Conservative
20131 (12bree1:024) Occupy Sea World (th) (th) ABC 122 dgreHldr F Liberal
20132 (12bree1:024) Occupy Sea World (tx) (tx) LOB 122 dgreHldr F Liberal
20133 (12bree1:025) [occupy sea world] shamu cartoon (th) (th) ABC 102 dgreHldr M Liberal
20134 (12bree1:025) [occupy sea world] shamu cartoon (tx) (tx) LOB 102 dgreHldr M Liberal
20135 (12bree1:025) occupy sea world [shamu] cartoon (ob) (ob) LOB 102 dgreHldr M Liberal
20136 (12bree1:025) occupy sea world shamu cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20137 (12bree1:026) [occupy sea world] cartoon (tx) (tx) LOB 110 nonDgreHldr M Moderate
20138 (12bree1:026) occupy sea world cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20139 (12bree1:026) occupy sea world cartoon (th) (th) ABC 110 nonDgreHldr M Moderate
20140 (12bree1:027) [occupy sea world with animals] cartoon (pr) (pr) VRE 106 nonDgreHldr F Moderate
20141 (12bree1:027) [occupy sea world with animals] cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20142 (12bree1:027) [occupy sea world] with animals cartoon (tx) (tx) LOB 106 nonDgreHldr F Moderate
294
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20143 (12bree1:027) occupy sea world with animals cartoon (fo) (fo) AHI 106 nonDgreHldr F Moderate
20144 (12bree1:028) occupy wall street (rf) (rf) ERE 101 nonDgreHldr F Conservative
20145 (12bree1:029) Occupy Wallstreet (rf) (rf) ERE 100 nonDgreHldr F Moderate
20146 (12bree1:030) Occupy Wallstreet (rf) (rf) ERE 124 dgreHldr F Liberal
20147 (12bree1:031) ocean (WTF) (WTF) WTF 121 dgreHldr M Moderate
20148 (12bree1:032) Orcas (ob) (ob) LOB 114 dgreHldr F Conservative
20149 (12bree1:033) orcas are slaves (tx) (tx) LOB 123 nonDgreHldr M Moderate
20150 (12bree1:034) peta (ss) (ss) PRA 119 nonDgreHldr M Moderate
20151 (12bree1:034) peta (th) (th) ABC 119 nonDgreHldr M Moderate
20152 (12bree1:034) peta (tx) (tx) LOB 119 nonDgreHldr M Moderate
20153 (12bree1:035) peta (ss) (ss) PRA 101 nonDgreHldr F Conservative
20154 (12bree1:035) peta (th) (th) ABC 101 nonDgreHldr F Conservative
20155 (12bree1:035) peta (tx) (tx) LOB 101 nonDgreHldr F Conservative
20156 (12bree1:036) PETA (ss) (ss) PRA 121 dgreHldr M Moderate
20157 (12bree1:036) PETA (th) (th) ABC 121 dgreHldr M Moderate
20158 (12bree1:036) PETA (tx) (tx) LOB 121 dgreHldr M Moderate
20159 (12bree1:037) Peta (ss) (ss) PRA 114 dgreHldr F Conservative
20160 (12bree1:037) Peta (th) (th) ABC 114 dgreHldr F Conservative
20161 (12bree1:037) Peta (tx) (tx) LOB 114 dgreHldr F Conservative
20162 (12bree1:038) PETA causes (th) (th) ABC 117 nonDgreHldr F Conservative
20163 (12bree1:039) politics (th) (th) ABC 119 nonDgreHldr M Moderate
20164 (12bree1:040) protest (ev) (ev) C/S 107 nonDgreHldr F Moderate
20165 (12bree1:041) sea animals defending themselves (pr) (pr) VRE 116 nonDgreHldr F Conservative
20166 (12bree1:042) sea animals defending themselves (th) (th) ABC 116 nonDgreHldr F Conservative
20167 (12bree1:043) [sea creatures vs peta] cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20168 (12bree1:044) [sea creatures] vs peta cartoon (ob) (ob) LOB 106 nonDgreHldr F Moderate
20169 (12bree1:045) sea creatures vs [peta] cartoon (ss) (ss) PRA 106 nonDgreHldr F Moderate
20170 (12bree1:046) sea creatures vs peta cartoon (fo) (fo) AHI 106 nonDgreHldr F Moderate
20171 (12bree1:047) [Sea mammals occupy wall street] spoof political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20172 (12bree1:047) [Sea mammals] occupy wall street spoof political cartoon (ob) (ob) LOB 112 nonDgreHldr F Moderate
20173 (12bree1:047) Sea mammals occupy wall street [spoof] political cartoon (ca) (ca) C/S 112 nonDgreHldr F Moderate
20174 (12bree1:047) Sea mammals occupy wall street spoof political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20175 (12bree1:048) sea world (ss) (ss) PRA 119 nonDgreHldr M Moderate
20176 (12bree1:048) sea world (th) (th) ABC 119 nonDgreHldr M Moderate
20177 (12bree1:048) sea world (tx) (tx) LOB 119 nonDgreHldr M Moderate
20178 (12bree1:049) Sea World (ss) (ss) PRA 117 nonDgreHldr F Conservative
20179 (12bree1:049) Sea World (th) (th) ABC 117 nonDgreHldr F Conservative
20180 (12bree1:049) Sea World (tx) (tx) LOB 117 nonDgreHldr F Conservative
295
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20181 (12bree1:050) Sea World (ss) (ss) PRA 114 dgreHldr F Conservative
20182 (12bree1:050) Sea World (th) (th) ABC 114 dgreHldr F Conservative
20183 (12bree1:050) Sea World (tx) (tx) LOB 114 dgreHldr F Conservative
20184 (12bree1:051) Sea World (ss) (ss) PRA 124 dgreHldr F Liberal
20185 (12bree1:051) Sea World (th) (th) ABC 124 dgreHldr F Liberal
20186 (12bree1:051) Sea World (tx) (tx) LOB 124 dgreHldr F Liberal
20187 (12bree1:052) sea world debate (ab) (ab) ABC 118 nonDgreHldr F Conservative
20188 (12bree1:052) sea world debate (ss) (ss) PRA 118 nonDgreHldr F Conservative
20189 (12bree1:053) Shamu (tx) (tx) LOB 114 dgreHldr F Conservative
20190 (12bree1:054) sharks (ob) (ob) LOB 124 dgreHldr F Liberal
20191 (12bree1:055) Slaves (tx) (tx) LOB 114 dgreHldr F Conservative
20192 (12bree1:056) [wall street vs sea world] parody (th) (th) ABC 105 dgreHldr M Liberal
20193 (12bree1:056) [wall street] vs sea world parody (rf) (rf) ERE 105 dgreHldr M Liberal
20194 (12bree1:056) [wall street] vs sea world parody (rf) (rf) ERE 105 dgreHldr M Liberal
20195 (12bree1:056) wall street vs [sea world] parody (tx) (tx) LOB 105 dgreHldr M Liberal
20196 (12bree1:056) wall street vs sea world parody (ca) (ca) C/S 105 dgreHldr M Liberal
20197 (12bree1:057) Whale conservation (WTF) (WTF) WTF 117 nonDgreHldr F Conservative
20198 (12bree1:058) what are we “occupying” for? (pr) (pr) VRE 108 nonDgreHldr F Moderate
20199 (13hand1:001) Airport Security (WTF) (WTF) WTF 100 nonDgreHldr F Moderate
20200 (13hand1:002) CARTOON (fo) (fo) AHI 106 nonDgreHldr F Moderate
20201 (13hand1:003) cheating on sat (th) (th) ABC 101 nonDgreHldr F Conservative
20202 (13hand1:004) comedy (ca) (ca) C/S 119 nonDgreHldr M Moderate
20203 (13hand1:005) criticism of secuity in US (th) (th) ABC 111 nonDgreHldr F Liberal
20204 (13hand1:006) Easier (tx) (tx) LOB 114 dgreHldr F Conservative
20205 (13hand1:007) funny S.A.T. joke (ca) (ca) C/S 105 dgreHldr M Liberal
20206 (13hand1:007) funny S.A.T. joke (th) (th) ABC 105 dgreHldr M Liberal
20207 (13hand1:008) high school cartoons (fo) (fo) AHI 117 nonDgreHldr F Conservative
20208 (13hand1:008) high school cartoons (th) (th) ABC 117 nonDgreHldr F Conservative
20209 (13hand1:009) high school security (th) (th) ABC 120 dgreHldr F Moderate
20210 (13hand1:010) Identification (WTF) (WTF) WTF 114 dgreHldr F Conservative
20211 (13hand1:011) impossible (tx) (tx) LOB 100 nonDgreHldr F Moderate
20212 (13hand1:012) [Kids] trying to take a test (pe) (pe) PEO 105 dgreHldr M Liberal
20213 (13hand1:012) [Kids] trying to take a test (ss) (ss) PRA 105 dgreHldr M Liberal
20214 (13hand1:012) Kids trying to take a test (ev) (ev) C/S 105 dgreHldr M Liberal
20215 (13hand1:012) Kids trying to take a test (pr) (pr) VRE 105 dgreHldr M Liberal
20216 (13hand1:012) Kids trying to take a test (th) (th) ABC 105 dgreHldr M Liberal
20217 (13hand1:013) Leagalization (WTF) (WTF) WTF 100 nonDgreHldr F Moderate
20218 (13hand1:014) lockdown on american security (th) (th) ABC 108 nonDgreHldr F Moderate
296
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20219 (13hand1:015) National security (th) (th) ABC 124 dgreHldr F Liberal
20220 (13hand1:016) nerves (ab) (ab) ABC 113 nonDgreHldr F Moderate
20221 (13hand1:017) personal invasion (pr) (pr) VRE 108 nonDgreHldr F Moderate
20222 (13hand1:017) personal invasion (th) (th) ABC 108 nonDgreHldr F Moderate
20223 (13hand1:018) preventative measure (th) (th) ABC 107 nonDgreHldr F Moderate
20224 (13hand1:019) SAT (th) (th) ABC 106 nonDgreHldr F Moderate
20225 (13hand1:019) SAT (tx) (tx) LOB 106 nonDgreHldr F Moderate
20226 (13hand1:020) SAT (th) (th) ABC 119 nonDgreHldr M Moderate
20227 (13hand1:020) SAT (tx) (tx) LOB 119 nonDgreHldr M Moderate
20228 (13hand1:021) sat (th) (th) ABC 113 nonDgreHldr F Moderate
20229 (13hand1:021) sat (tx) (tx) LOB 113 nonDgreHldr F Moderate
20230 (13hand1:022) SAT (th) (th) ABC 100 nonDgreHldr F Moderate
20231 (13hand1:022) SAT (tx) (tx) LOB 100 nonDgreHldr F Moderate
20232 (13hand1:023) SAT (th) (th) ABC 118 nonDgreHldr F Conservative
20233 (13hand1:023) SAT (tx) (tx) LOB 118 nonDgreHldr F Conservative
20234 (13hand1:024) SAT (th) (th) ABC 109 dgreHldr M Moderate
20235 (13hand1:024) SAT (tx) (tx) LOB 109 dgreHldr M Moderate
20236 (13hand1:025) sat cartoon (fo) (fo) AHI 123 nonDgreHldr M Moderate
20237 (13hand1:025) sat cartoon (th) (th) ABC 123 nonDgreHldr M Moderate
20238 (13hand1:026) SAT cartoons (fo) (fo) AHI 118 nonDgreHldr F Conservative
20239 (13hand1:026) SAT cartoons (th) (th) ABC 118 nonDgreHldr F Conservative
20240 (13hand1:027) SAT prep (th) (th) ABC 117 nonDgreHldr F Conservative
20241 (13hand1:028) sat security screening (th) (th) ABC 123 nonDgreHldr M Moderate
20242 (13hand1:029) sat testing (th) (th) ABC 101 nonDgreHldr F Conservative
20243 (13hand1:029) sat testing (tx) (tx) LOB 101 nonDgreHldr F Conservative
20244 (13hand1:030) SAT Testing (th) (th) ABC 114 dgreHldr F Conservative
20245 (13hand1:030) SAT Testing (tx) (tx) LOB 114 dgreHldr F Conservative
20246 (13hand1:031) SAT testing (th) (th) ABC 120 dgreHldr F Moderate
20247 (13hand1:031) SAT testing (tx) (tx) LOB 120 dgreHldr F Moderate
20248 (13hand1:032) sat testing easier than security cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20249 (13hand1:032) sat testing easier than security cartoon (pr) (pr) VRE 110 nonDgreHldr M Moderate
20250 (13hand1:033) security (th) (th) ABC 101 nonDgreHldr F Conservative
20251 (13hand1:033) security (tx) (tx) LOB 101 nonDgreHldr F Conservative
20252 (13hand1:034) Security (th) (th) ABC 114 dgreHldr F Conservative
20253 (13hand1:034) Security (tx) (tx) LOB 114 dgreHldr F Conservative
20254 (13hand1:035) security (th) (th) ABC 109 dgreHldr M Moderate
20255 (13hand1:035) security (tx) (tx) LOB 109 dgreHldr M Moderate
20256 (13hand1:036) security crossing the line (pr) (pr) VRE 108 nonDgreHldr F Moderate
297
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20257 (13hand1:036) security crossing the line (th) (th) ABC 108 nonDgreHldr F Moderate
20258 (13hand1:037) security in the US (th) (th) ABC 111 nonDgreHldr F Liberal
20259 (13hand1:038) [security interrogating] and SAT spoof political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20260 (13hand1:038) security interrogating and [SAT] spoof political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20261 (13hand1:038) security interrogating and [SAT] spoof political cartoon (tx) (tx) LOB 112 nonDgreHldr F Moderate
20262 (13hand1:038) security interrogating and SAT spoof political cartoon (ca) (ca) C/S 112 nonDgreHldr F Moderate
20263 (13hand1:038) security interrogating and SAT spoof political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20264 (13hand1:039) security is annoying (pr) (pr) VRE 116 nonDgreHldr F Conservative
20265 (13hand1:039) security is annoying (th) (th) ABC 116 nonDgreHldr F Conservative
20266 (13hand1:040) black (WTF) (WTF) WTF 116 nonDgreHldr F Conservative
20267 (13hand1:041) [security screening] SAT cartoon (th) (th) ABC 102 dgreHldr M Liberal
20268 (13hand1:041) security screening [SAT] cartoon (th) (th) ABC 102 dgreHldr M Liberal
20269 (13hand1:041) security screening [SAT] cartoon (tx) (tx) LOB 102 dgreHldr M Liberal
20270 (13hand1:041) security screening SAT cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20271 (13hand1:042) standardized testing (th) (th) ABC 121 dgreHldr M Moderate
20272 (13hand1:043) Standardized testing (th) (th) ABC 124 dgreHldr F Liberal
20273 (13hand1:044) Standardized Testing Issues (th) (th) ABC 115 nonDgreHldr M Liberal
20274 (13hand1:045) students (pe) (pe) PEO 119 nonDgreHldr M Moderate
20275 (13hand1:045) students (ss) (ss) PRA 119 nonDgreHldr M Moderate
20276 (13hand1:046) [teen boys] sat (pe) (pe) PEO 106 nonDgreHldr F Moderate
20277 (13hand1:046) [teen boys] sat (ss) (ss) PRA 106 nonDgreHldr F Moderate
20278 (13hand1:046) teen boys [sat] (th) (th) ABC 106 nonDgreHldr F Moderate
20279 (13hand1:046) teen boys [sat] (tx) (tx) LOB 106 nonDgreHldr F Moderate
20280 (13hand1:047) terrorism (WTF) (WTF) WTF 107 nonDgreHldr F Moderate
20281 (13hand1:048) test cheating (th) (th) ABC 120 dgreHldr F Moderate
20282 (13hand1:049) Test joke (ca) (ca) C/S 105 dgreHldr M Liberal
20283 (13hand1:049) Test joke (th) (th) ABC 105 dgreHldr M Liberal
20284 (13hand1:050) Tricky (tx) (tx) LOB 114 dgreHldr F Conservative
20285 (13hand1:051) [TSA] cartoon spoof (WTF) (WTF) WTF 104 nonDgreHldr M Moderate
20286 (13hand1:051) TSA cartoon spoof (ca) (ca) C/S 104 nonDgreHldr M Moderate
20287 (13hand1:051) TSA cartoon spoof (fo) (fo) AHI 104 nonDgreHldr M Moderate
20288 (13hand1:052) [TSA] vs SAT (WTF) (WTF) WTF 122 dgreHldr F Liberal
20289 (13hand1:052) TSA vs [SAT] (th) (th) ABC 122 dgreHldr F Liberal
20290 (13hand1:052) TSA vs [SAT] (tx) (tx) LOB 122 dgreHldr F Liberal
20291 (13hand1:052) TSA vs SAT (pr) (pr) VRE 122 dgreHldr F Liberal
20292 (13hand1:053) typical everywhere (pr) (pr) VRE 103 nonDgreHldr F Moderate
20293 (13hand1:054) why does it have to be so hard (pr) (pr) VRE 105 dgreHldr M Liberal
20294 (14luck1:001) Accomplished (tx) (tx) LOB 114 dgreHldr F Conservative
298
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20295 (14luck1:002) [banner] obama bush Iraq cartoon (ob) (ob) LOB 102 dgreHldr M Liberal
20296 (14luck1:002) banner [obama] bush Iraq cartoon (pe) (pe) PEO 102 dgreHldr M Liberal
20297 (14luck1:002) banner obama [bush] Iraq cartoon (pe) (pe) PEO 102 dgreHldr M Liberal
20298 (14luck1:002) banner obama bush [Iraq] cartoon (th) (th) ABC 102 dgreHldr M Liberal
20299 (14luck1:002) banner obama bush Iraq cartoon (th) (th) ABC 102 dgreHldr M Liberal
20300 (14luck1:002) banner obama bush Iraq cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20301 (14luck1:003) blunder (tx) (tx) LOB 121 dgreHldr M Moderate
20302 (14luck1:004) bush (pe) (pe) PEO 113 nonDgreHldr F Moderate
20303 (14luck1:005) bush (pe) (pe) PEO 109 dgreHldr M Moderate
20304 (14luck1:006) bush (pe) (pe) PEO 101 nonDgreHldr F Conservative
20305 (14luck1:007) bush jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20306 (14luck1:007) bush jokes (th) (th) ABC 105 dgreHldr M Liberal
20307 (14luck1:007) bush jokes (pe) (pe) PEO 105 dgreHldr M Liberal
20308 (14luck1:008) [Bush] Obama Misson Accomplished (pe) (pe) PEO 115 nonDgreHldr M Liberal
20309 (14luck1:008) Bush [Obama] Misson Accomplished (pe) (pe) PEO 115 nonDgreHldr M Liberal
20310 (14luck1:008) Bush Obama [Misson Accomplished] (rf) (rf) ERE 115 nonDgreHldr M Liberal
20311 (14luck1:009) bush vs obama jokes (pr) (pr) VRE 105 dgreHldr M Liberal
20312 (14luck1:009) bush vs obama jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20313 (14luck1:009) bush vs obama jokes (th) (th) ABC 105 dgreHldr M Liberal
20314 (14luck1:010) bush vs. obama (pr) (pr) VRE 108 nonDgreHldr F Moderate
20315 (14luck1:010) bush vs. obama (th) (th) ABC 108 nonDgreHldr F Moderate
20316 (14luck1:011) [bush] watching obama on iraq (pe) (pe) PEO 106 nonDgreHldr F Moderate
20317 (14luck1:011) bush watching [obama] on iraq (pe) (pe) PEO 106 nonDgreHldr F Moderate
20318 (14luck1:011) bush watching obama on iraq (pr) (pr) VRE 106 nonDgreHldr F Moderate
20319 (14luck1:011) bush watching obama on iraq (pr) (pr) VRE 106 nonDgreHldr F Moderate
20320 (14luck1:011) bush watching obama on iraq (th) (th) ABC 106 nonDgreHldr F Moderate
20321 (14luck1:011) bush watching obama on iraq (th) (th) ABC 106 nonDgreHldr F Moderate
20322 (14luck1:012) bush white house (th) (th) ABC 123 nonDgreHldr M Moderate
20323 (14luck1:012) bush white house (th) (th) ABC 123 nonDgreHldr M Moderate
20324 (14luck1:012) bush white house (pe) (pe) PEO 123 nonDgreHldr M Moderate
20325 (14luck1:012) bush white house (pe) (pe) PEO 123 nonDgreHldr M Moderate
20326 (14luck1:013) cartoon (fo) (fo) AHI 119 nonDgreHldr M Moderate
20327 (14luck1:014) current status of Iraq (pr) (pr) VRE 111 nonDgreHldr F Liberal
20328 (14luck1:014) current status of Iraq (th) (th) ABC 111 nonDgreHldr F Liberal
20329 (14luck1:015) deceit (at) (at) ABC 107 nonDgreHldr F Moderate
20330 (14luck1:016) democrat (ab) (ab) ABC 113 nonDgreHldr F Moderate
20331 (14luck1:017) exit does not equal accomplishment (pr) (pr) VRE 121 dgreHldr M Moderate
20332 (14luck1:018) Exit strategy (th) (th) ABC 124 dgreHldr F Liberal
299
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20333 (14luck1:019) George Bush (pe) (pe) PEO 114 dgreHldr F Conservative
20334 (14luck1:020) George W Bush (pe) (pe) PEO 120 dgreHldr F Moderate
20335 (14luck1:021) george w. bush (pe) (pe) PEO 119 nonDgreHldr M Moderate
20336 (14luck1:022) George W. Bush (pe) (pe) PEO 117 nonDgreHldr F Conservative
20337 (14luck1:023) George W. Bush (pe) (pe) PEO 124 dgreHldr F Liberal
20338 (14luck1:024) George W. Bush vs. Obama (th) (th) ABC 118 nonDgreHldr F Conservative
20339 (14luck1:025) Iraq (th) (th) ABC 114 dgreHldr F Conservative
20340 (14luck1:026) iraq (th) (th) ABC 109 dgreHldr M Moderate
20341 (14luck1:027) Iraq War (th) (th) ABC 124 dgreHldr F Liberal
20342 (14luck1:028) jealousy of obamas campaign from bush (pr) (pr) VRE 116 nonDgreHldr F Conservative
20343 (14luck1:028) jealousy of obamas campaign from bush (th) (th) ABC 116 nonDgreHldr F Conservative
20344 (14luck1:029) Mission (tx) (tx) LOB 121 dgreHldr M Moderate
20345 (14luck1:030) Mission (tx) (tx) LOB 114 dgreHldr F Conservative
20346 (14luck1:031) Mission Accomplished (rf) (rf) ERE 100 nonDgreHldr F Moderate
20347 (14luck1:032) mission accomplished (rf) (rf) ERE 102 dgreHldr M Liberal
20348 (14luck1:033) mission accomplished (rf) (rf) ERE 120 dgreHldr F Moderate
20349 (14luck1:034) mission accomplished (rf) (rf) ERE 109 dgreHldr M Moderate
20350 (14luck1:035) Mission Accomplished Banner (rf) (rf) ERE 122 dgreHldr F Liberal
20351 (14luck1:036) [mission accomplished] bush obama cartoon (rf) (rf) ERE 106 nonDgreHldr F Moderate
20352 (14luck1:036) mission accomplished [bush] obama cartoon (pe) (pe) PEO 106 nonDgreHldr F Moderate
20353 (14luck1:036) mission accomplished bush [obama] cartoon (pe) (pe) PEO 106 nonDgreHldr F Moderate
20354 (14luck1:036) mission accomplished bush obama cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20355 (14luck1:036) mission accomplished bush obama cartoon (fo) (fo) AHI 106 nonDgreHldr F Moderate
20356 (14luck1:037) obama (pe) (pe) PEO 119 nonDgreHldr M Moderate
20357 (14luck1:038) obama (pe) (pe) PEO 113 nonDgreHldr F Moderate
20358 (14luck1:039) Obama (pe) (pe) PEO 117 nonDgreHldr F Conservative
20359 (14luck1:040) Obama (pe) (pe) PEO 114 dgreHldr F Conservative
20360 (14luck1:041) Obama (pe) (pe) PEO 124 dgreHldr F Liberal
20361 (14luck1:042) obama (pe) (pe) PEO 109 dgreHldr M Moderate
20362 (14luck1:043) obama and his stance on the war (pr) (pr) VRE 101 nonDgreHldr F Conservative
20363 (14luck1:043) obama and his stance on the war (th) (th) ABC 101 nonDgreHldr F Conservative
20364 (14luck1:044) [obama] banner vs. bush banner (pe) (pe) PEO 104 nonDgreHldr M Moderate
20365 (14luck1:044) obama banner vs. [bush] banner (pe) (pe) PEO 104 nonDgreHldr M Moderate
20366 (14luck1:044) obama banner vs. bush banner (pr) (pr) VRE 104 nonDgreHldr M Moderate
20367 (14luck1:044) obama banner vs. bush banner (th) (th) ABC 104 nonDgreHldr M Moderate
20368 (14luck1:045) [obama] bush political cartoon (pe) (pe) PEO 123 nonDgreHldr M Moderate
20369 (14luck1:045) obama [bush] political cartoon (pe) (pe) PEO 123 nonDgreHldr M Moderate
20370 (14luck1:045) obama bush [political cartoon] (th) (th) ABC 123 nonDgreHldr M Moderate
300
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20371 (14luck1:045) obama bush [political cartoon] (fo) (fo) AHI 123 nonDgreHldr M Moderate
20372 (14luck1:046) obama fails at presidency (pr) (pr) VRE 108 nonDgreHldr F Moderate
20373 (14luck1:046) obama fails at presidency (th) (th) ABC 108 nonDgreHldr F Moderate
20374 (14luck1:047) obama looks for a way out (pr) (pr) VRE 108 nonDgreHldr F Moderate
20375 (14luck1:047) obama looks for a way out (th) (th) ABC 108 nonDgreHldr F Moderate
20376 (14luck1:048) [obama vs bush] cartoon (th) (th) ABC 123 nonDgreHldr M Moderate
20377 (14luck1:048) [obama vs bush] cartoon (fo) (fo) AHI 123 nonDgreHldr M Moderate
20378 (14luck1:049) [Obama vs Bush] political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20379 (14luck1:049) [Obama vs Bush] political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20380 (14luck1:059) Obama vs Bush political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20381 (14luck1:059) Obama vs Bush political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20382 (14luck1:059) Obama vs Bush political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20383 (14luck1:060) operation iraqi freedom satire (pr) (pr) VRE 105 dgreHldr M Liberal
20384 (14luck1:060) operation iraqi freedom satire (ca) (ca) C/S 105 dgreHldr M Liberal
20385 (14luck1:060) operation iraqi freedom satire (th) (th) ABC 105 dgreHldr M Liberal
20386 (14luck1:061) our mission never works when it comes to that (pr) (pr) VRE 103 nonDgreHldr F Moderate
20387 (14luck1:062) plane (ob) (ob) LOB 100 nonDgreHldr F Moderate
20388 (14luck1:063) President Bush (pe) (pe) PEO 121 dgreHldr M Moderate
20389 (14luck1:064) Presidential campaign (th) (th) ABC 117 nonDgreHldr F Conservative
20390 (14luck1:065) republican (ab) (ab) ABC 113 nonDgreHldr F Moderate
20391 (14luck1:066) [tiny bush] cartoon mike luckonich (de) (de) DES 110 nonDgreHldr M Moderate
20392 (14luck1:066) [tiny bush] cartoon mike luckonich (pe) (pe) PEO 110 nonDgreHldr M Moderate
20393 (14luck1:066) tiny bush [cartoon] mike luckonich (fo) (fo) AHI 110 nonDgreHldr M Moderate
20394 (14luck1:066) tiny bush cartoon [mike luckonich] (ar) (ar) AHI 110 nonDgreHldr M Moderate
20395 (14luck1:067) war jokes iraq (ca) (ca) C/S 105 dgreHldr M Liberal
20396 (14luck1:067) war jokes iraq (th) (th) ABC 105 dgreHldr M Liberal
20397 (15rame1:001) Air force one (ob) (ob) LOB 100 nonDgreHldr F Moderate
20398 (15rame1:002) air force one (ob) (ob) LOB 123 nonDgreHldr M Moderate
20399 (15rame1:003) air force one (ob) (ob) LOB 117 nonDgreHldr F Conservative
20400 (15rame1:004) Air Force One (ob) (ob) LOB 102 dgreHldr M Liberal
20401 (15rame1:005) Air Force One (ob) (ob) LOB 124 dgreHldr F Liberal
20402 (15rame1:006) air force one (ob) (ob) LOB 109 dgreHldr M Moderate
20403 (15rame1:007) [air force one] cartoon (ob) (ob) LOB 123 nonDgreHldr M Moderate
20404 (15rame1:007) air force one cartoon (fo) (fo) AHI 123 nonDgreHldr M Moderate
20405 (15rame1:008) [air force one] joke (ob) (ob) LOB 105 dgreHldr M Liberal
20406 (15rame1:008) air force one joke (ca) (ca) C/S 105 dgreHldr M Liberal
20407 (15rame1:009) [air force one] taxpaers cartoon (ob) (ob) LOB 110 nonDgreHldr M Moderate
20408 (15rame1:009) air force one [taxpaers] cartoon (ss) (ss) PRA 110 nonDgreHldr M Moderate
301
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20409 (15rame1:009) air force one [taxpaers] cartoon (PEO) (PEO) PEO 110 nonDgreHldr M Moderate
20410 (15rame1:009) air force one taxpaers cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20411 (15rame1:010) [air force one] taxpayers (ob) (ob) LOB 123 nonDgreHldr M Moderate
20412 (15rame1:010) air force one [taxpayers] (ss) (ss) PRA 123 nonDgreHldr M Moderate
20413 (15rame1:010) air force one [taxpayers] (PEO) (PEO) PEO 123 nonDgreHldr M Moderate
20414 (15rame1:011) [Air Force One] Taxpayers (ob) (ob) LOB 122 dgreHldr F Liberal
20415 (15rame1:011) Air Force One [Taxpayers] (ss) (ss) PRA 122 dgreHldr F Liberal
20416 (15rame1:011) Air Force One [Taxpayers] (PEO) (PEO) PEO 122 dgreHldr F Liberal
20417 (15rame1:012) [airforce one] tax payer tonight show (ob) (ob) LOB 106 nonDgreHldr F Moderate
20418 (15rame1:012) airforce one [tax payer] tonight show (ss) (ss) PRA 106 nonDgreHldr F Moderate
20419 (15rame1:012) airforce one [tax payer] tonight show (PEO) (PEO) PEO 106 nonDgreHldr F Moderate
20420 (15rame1:012) airforce one tax payer [tonight show] (rf) (rf) ERE 106 nonDgreHldr F Moderate
20421 (15rame1:013) airlines (WTF) (WTF) WTF 119 nonDgreHldr M Moderate
20422 (15rame1:014) Airplane (ob) (ob) LOB 114 dgreHldr F Conservative
20423 (15rame1:015) [airplane] tax joke (ob) (ob) LOB 116 nonDgreHldr F Conservative
20424 (15rame1:015) airplane [tax joke] (ca) (ca) C/S 116 nonDgreHldr F Conservative
20425 (15rame1:016) allocating tax money for vacation (pr) (pr) VRE 108 nonDgreHldr F Moderate
20426 (15rame1:016) allocating tax money for vacation (th) (th) ABC 108 nonDgreHldr F Moderate
20427 (15rame1:017) America wasting resources (pr) (pr) VRE 108 nonDgreHldr F Moderate
20428 (15rame1:017) America wasting resources (th) (th) ABC 108 nonDgreHldr F Moderate
20429 (15rame1:018) budget (ab) (ab) ABC 109 dgreHldr M Moderate
20430 (15rame1:019) [careless spending] joke (pr) (pr) VRE 105 dgreHldr M Liberal
20431 (15rame1:019) [careless spending] joke (th) (th) ABC 105 dgreHldr M Liberal
20432 (15rame1:019) careless spending joke (ca) (ca) C/S 105 dgreHldr M Liberal
20433 (15rame1:020) corrupt (at) (at) ABC 103 nonDgreHldr F Moderate
20434 (15rame1:021) do we really need to spend that money (pr) (pr) VRE 105 dgreHldr M Liberal
20435 (15rame1:022) financial crisis (th) (th) ABC 117 nonDgreHldr F Conservative
20436 (15rame1:023) Flotation Device (tx) (tx) LOB 114 dgreHldr F Conservative
20437 (15rame1:024) Frivolous government spending (pr) (pr) VRE 124 dgreHldr F Liberal
20438 (15rame1:024) Frivolous government spending (th) (th) ABC 124 dgreHldr F Liberal
20439 (15rame1:025) Frivolous Trip (tx) (tx) LOB 114 dgreHldr F Conservative
20440 (15rame1:026) government spending (th) (th) ABC 117 nonDgreHldr F Conservative
20441 (15rame1:026) government spending (th) (th) ABC 121 dgreHldr M Moderate
20442 (15rame1:027) inflated government spending (pr) (pr) VRE 120 dgreHldr F Moderate
20443 (15rame1:027) inflated government spending (th) (th) ABC 120 dgreHldr F Moderate
20444 (15rame1:028) parody (ca) (ca) C/S 119 nonDgreHldr M Moderate
20445 (15rame1:029) Political cartoon on taxpayers (fo) (fo) AHI 112 nonDgreHldr F Moderate
20446 (15rame1:029) Political cartoon on [taxpayers] (ss) (ss) PRA 112 nonDgreHldr F Moderate
302
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20447 (15rame1:029) Political cartoon on [taxpayers] (PEO) (PEO) PEO 112 nonDgreHldr F Moderate
20448 (15rame1:030) president obama (th) (th) ABC 121 dgreHldr M Moderate
20449 (15rame1:030) president obama (PEO) (PEO) PEO 121 dgreHldr M Moderate
20450 (15rame1:031) President of the United States (th) (th) ABC 124 dgreHldr F Liberal
20451 (15rame1:031) President of the United States (ss) (ss) PRA 124 dgreHldr F Liberal
20452 (15rame1:032) President of the United States (PEO) (PEO) PEO 124 dgreHldr F Liberal
20453 (15rame1:033) taking advantage of taxpayer dollars (pr) (pr) VRE 108 nonDgreHldr F Moderate
20454 (15rame1:032) taking advantage of taxpayer dollars (th) (th) ABC 108 nonDgreHldr F Moderate
20455 (15rame1:033) tax on wealthy (pr) (pr) VRE 111 nonDgreHldr F Liberal
20456 (15rame1:033) tax on wealthy (th) (th) ABC 111 nonDgreHldr F Liberal
20457 (15rame1:034) tax payers wallets are empty (pr) (pr) VRE 101 nonDgreHldr F Conservative
20458 (15rame1:034) tax payers wallets are empty (th) (th) ABC 101 nonDgreHldr F Conservative
20459 (15rame1:035) taxes (th) (th) ABC 119 nonDgreHldr M Moderate
20460 (15rame1:036) taxes (th) (th) ABC 113 nonDgreHldr F Moderate
20461 (15rame1:037) taxes (th) (th) ABC 121 dgreHldr M Moderate
20462 (15rame1:038) taxes (th) (th) ABC 109 dgreHldr M Moderate
20463 (15rame1:039) taxpayer struggles (pr) (pr) VRE 111 nonDgreHldr F Liberal
20464 (15rame1:039) taxpayer struggles (th) (th) ABC 111 nonDgreHldr F Liberal
20465 (15rame1:040) taxpayers (th) (th) ABC 100 nonDgreHldr F Moderate
20466 (15rame1:040) taxpayers (ss) (ss) PRA 100 nonDgreHldr F Moderate
20467 (15rame1:041) taxpayers (PEO) (PEO) PEO 100 nonDgreHldr F Moderate
20468 (15rame1:041) Taxpayers (th) (th) ABC 114 dgreHldr F Conservative
20469 (15rame1:041) Taxpayers (ss) (ss) PRA 114 dgreHldr F Conservative
20470 (15rame1:042) Taxpayers (PEO) (PEO) PEO 114 dgreHldr F Conservative
20471 (15rame1:042) [taxpayers] cartoon (th) (th) ABC 102 dgreHldr M Liberal
20472 (15rame1:042) [taxpayers] cartoon (ss) (ss) PRA 102 dgreHldr M Liberal
20473 (15rame1:043) [taxpayers] cartoon (PEO) (PEO) PEO 102 dgreHldr M Liberal
20474 (15rame1:042) taxpayers cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20475 (15rame1:043) Taxpayers' Waste (pr) (pr) VRE 115 nonDgreHldr M Liberal
20476 (15rame1:043) Taxpayers' Waste (th) (th) ABC 115 nonDgreHldr M Liberal
20477 (15rame1:044) tonight show (rf) (rf) ERE 119 nonDgreHldr M Moderate
20478 (15rame1:044) tonight show (tx) (tx) LOB 119 nonDgreHldr M Moderate
20479 (15rame1:045) Tonight Show (rf) (rf) ERE 102 dgreHldr M Liberal
20480 (15rame1:045) Tonight Show (tx) (tx) LOB 102 dgreHldr M Liberal
20481 (15rame1:046) Tonight Show (rf) (rf) ERE 124 dgreHldr F Liberal
20482 (15rame1:046) Tonight Show (tx) (tx) LOB 124 dgreHldr F Liberal
20483 (15rame1:047) [U.S. spending] 2011 (pr) (pr) VRE 120 dgreHldr F Moderate
20484 (15rame1:047) [U.S. spending] 2011 (th) (th) ABC 120 dgreHldr F Moderate
303
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20485 (15rame1:047) U.S. spending [2011] (ta) (ta) C/S 120 dgreHldr F Moderate
20486 (15rame1:048) United States of America (th) (th) ABC 114 dgreHldr F Conservative
20487 (15rame1:048) United States of America (tx) (tx) LOB 114 dgreHldr F Conservative
20488 (15rame1:049) [US] and Tax problems (th) (th) ABC 118 nonDgreHldr F Conservative
20489 (15rame1:049) US and [Tax problems] (pr) (pr) VRE 118 nonDgreHldr F Conservative
20490 (15rame1:049) US and [Tax problems] (th) (th) ABC 118 nonDgreHldr F Conservative
20491 (15rame1:050) Us tax breaks (pr) (pr) VRE 111 nonDgreHldr F Liberal
20492 (15rame1:050) Us tax breaks (th) (th) ABC 111 nonDgreHldr F Liberal
20493 (15rame1:051) [US tax payer funded plane] cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20494 (15rame1:051) [US tax payer funded plane] cartoon (ob) (ob) LOB 106 nonDgreHldr F Moderate
20495 (15rame1:051) US tax payer funded plane cartoon (fo) (fo) AHI 106 nonDgreHldr F Moderate
20496 (15rame1:052) usa (th) (th) ABC 119 nonDgreHldr M Moderate
20497 (15rame1:053) USA (th) (th) ABC 100 nonDgreHldr F Moderate
20498 (15rame1:054) [usa] wastin taxpayer money (th) (th) ABC 105 dgreHldr M Liberal
20499 (15rame1:054) usa wastin taxpayer money (pr) (pr) VRE 105 dgreHldr M Liberal
20500 (15rame1:054) usa wastin taxpayer money (th) (th) ABC 105 dgreHldr M Liberal
20501 (15rame1:055) Wallers (tx) (tx) LOB 114 dgreHldr F Conservative
20502 (15rame1:056) wallets as flotation devices (pr) (pr) VRE 104 nonDgreHldr M Moderate
20503 (15rame1:056) wallets as flotation devices (th) (th) ABC 104 nonDgreHldr M Moderate
20504 (15rame1:057) waste of money (pr) (pr) VRE 103 nonDgreHldr F Moderate
20505 (15rame1:057) waste of money (th) (th) ABC 103 nonDgreHldr F Moderate
20506 (16ande2:001) americas real default problem (pr) (pr) VRE 105 dgreHldr M Liberal
20507 (16ande2:001) americas real default problem (th) (th) ABC 105 dgreHldr M Liberal
20508 (16ande2:002) [are we going to claim bankruptcy] jokes (pr) (pr) VRE 105 dgreHldr M Liberal
20509 (16ande2:002) [are we going to claim bankruptcy] jokes (th) (th) ABC 105 dgreHldr M Liberal
20510 (16ande2:002) are we going to claim bankruptcy jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20511 (16ande2:003) Axes (ob) (ob) LOB 100 nonDgreHldr F Moderate
20512 (16ande2:003) Axes (ob) (ob) LOB 114 dgreHldr F Conservative
20513 (16ande2:004) Committee (PEO) (PEO) PEO 114 dgreHldr F Conservative
20514 (16ande2:004) Committee (ss) (ss) PRA 114 dgreHldr F Conservative
20515 (16ande2:004) Committee (tx) (tx) LOB 114 dgreHldr F Conservative
20516 (16ande2:005) committee elephant (ob) (ob) LOB 110 nonDgreHldr M Moderate
20517 (16ande2:006) committee no doing its job (pr) (pr) VRE 121 dgreHldr M Moderate
20518 (16ande2:006) committee no doing its job (th) (th) ABC 121 dgreHldr M Moderate
20519 (16ande2:007) deficit (th) (th) ABC 113 nonDgreHldr F Moderate
20520 (16ande2:007) deficit (tx) (tx) LOB 113 nonDgreHldr F Moderate
20521 (16ande2:008) Deficit (th) (th) ABC 114 dgreHldr F Conservative
20522 (16ande2:008) Deficit (tx) (tx) LOB 114 dgreHldr F Conservative
304
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20523 (16ande2:009) Deficit (th) (th) ABC 124 dgreHldr F Liberal
20524 (16ande2:009) Deficit (tx) (tx) LOB 124 dgreHldr F Liberal
20525 (16ande2:010) [Deficit] 2011 (th) (th) ABC 120 dgreHldr F Moderate
20526 (16ande2:010) [Deficit] 2011 (tx) (tx) LOB 120 dgreHldr F Moderate
20527 (16ande2:010) Deficit [2011] (ta) (ta) C/S 120 dgreHldr F Moderate
20528 (16ande2:011) deficit commitee (PEO) (PEO) PEO 101 nonDgreHldr F Conservative
20529 (16ande2:011) deficit commitee (ss) (ss) PRA 101 nonDgreHldr F Conservative
20530 (16ande2:011) deficit commitee (tx) (tx) LOB 101 nonDgreHldr F Conservative
20531 (16ande2:012) deficit committee (PEO) (PEO) PEO 123 nonDgreHldr M Moderate
20532 (16ande2:012) deficit committee (ss) (ss) PRA 123 nonDgreHldr M Moderate
20533 (16ande2:012) deficit committee (tx) (tx) LOB 123 nonDgreHldr M Moderate
20534 (16ande2:013) [deficit committee] thanksgiving turkey cartoon (PEO) (PEO) PEO 102 dgreHldr M Liberal
20535 (16ande2:013) [deficit committee] thanksgiving turkey cartoon (ss) (ss) PRA 102 dgreHldr M Liberal
20536 (16ande2:013) [deficit committee] thanksgiving turkey cartoon (tx) (tx) LOB 102 dgreHldr M Liberal
20537 (16ande2:013) deficit committee [thanksgiving] turkey cartoon (ta) (ta) C/S 102 dgreHldr M Liberal
20538 (16ande2:013) deficit committee thanksgiving [turkey] cartoon (ob) (ob) LOB 102 dgreHldr M Liberal
20539 (16ande2:013) deficit committee thanksgiving turkey cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20540 (16ande2:014) [deficit] donkey (tx) (tx) LOB 110 nonDgreHldr M Moderate
20541 (16ande2:014) deficit [donkey] (ob) (ob) LOB 110 nonDgreHldr M Moderate
20542 (16ande2:015) Deficit Growing (pr) (pr) VRE 121 dgreHldr M Moderate
20543 (16ande2:015) Deficit Growing (th) (th) ABC 121 dgreHldr M Moderate
20544 (16ande2:016) deficit in the US (pr) (pr) VRE 111 nonDgreHldr F Liberal
20545 (16ande2:016) deficit in the US (th) (th) ABC 111 nonDgreHldr F Liberal
20546 (16ande2:017) deficit jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20547 (16ande2:017) deficit jokes (th) (th) ABC 105 dgreHldr M Liberal
20548 (16ande2:018) [deficit] turkey (th) (th) ABC 110 nonDgreHldr M Moderate
20549 (16ande2:018) deficit [turkey] (ob) (ob) LOB 110 nonDgreHldr M Moderate
20550 (16ande2:019) [deficit] turkey and pilgrim republican and democrat cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20551 (16ande2:019) [deficit] turkey and pilgrim republican and democrat cartoon (tx) (tx) LOB 106 nonDgreHldr F Moderate
20552 (16ande2:019) deficit [turkey] and pilgrim republican and democrat cartoon (ob) (ob) LOB 106 nonDgreHldr F Moderate
20553 (16ande2:019) deficit turkey and [pilgrim republican and democrat] cartoon (PEO) (PEO) PEO 106 nonDgreHldr F Moderate
20554 (16ande2:019) deficit turkey and [pilgrim republican and democrat] cartoon (ss) (ss) PRA 106 nonDgreHldr F Moderate
20555 (16ande2:019) deficit turkey and pilgrim republican and democrat cartoon (fo) (fo) AHI 106 nonDgreHldr F Moderate
20556 (16ande2:020) democratic stance on deficit (pr) (pr) VRE 111 nonDgreHldr F Liberal
20557 (16ande2:020) democratic stance on deficit (th) (th) ABC 111 nonDgreHldr F Liberal
20558 (16ande2:021) economic spoof (ca) (ca) C/S 107 nonDgreHldr F Moderate
20559 (16ande2:021) economic spoof (th) (th) ABC 107 nonDgreHldr F Moderate
20560 (16ande2:022) economy (th) (th) ABC 113 nonDgreHldr F Moderate
305
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20561 (16ande2:023) Foreign Policy (WTF) (WTF) WTF 100 nonDgreHldr F Moderate
20562 (16ande2:024) growing defict (pr) (pr) VRE 101 nonDgreHldr F Conservative
20563 (16ande2:024) growing defict (th) (th) ABC 101 nonDgreHldr F Conservative
20564 (16ande2:025) how big is our deficit (pr) (pr) VRE 105 dgreHldr M Liberal
20565 (16ande2:025) how big is our deficit (th) (th) ABC 105 dgreHldr M Liberal
20566 (16ande2:026) increasing deficit (th) (th) ABC 108 nonDgreHldr F Moderate
20567 (16ande2:027) large deficit (th) (th) ABC 101 nonDgreHldr F Conservative
20568 (16ande2:028) paradoy (ca) (ca) C/S 119 nonDgreHldr M Moderate
20569 (16ande2:029) political cartoon (fo) (fo) AHI 119 nonDgreHldr M Moderate
20570 (16ande2:029) political cartoon (fo) (fo) AHI 107 nonDgreHldr F Moderate
20571 (16ande2:030) political cartoon of national budget deficit (fo) (fo) AHI 112 nonDgreHldr F Moderate
20572 (16ande2:030) political cartoon of national budget deficit (th) (th) ABC 112 nonDgreHldr F Moderate
20573 (16ande2:031) political cartoons (fo) (fo) AHI 117 nonDgreHldr F Conservative
20574 (16ande2:032) political party pilgrims (PEO) (PEO) PEO 123 nonDgreHldr M Moderate
20575 (16ande2:032) political party pilgrims (ss) (ss) PRA 123 nonDgreHldr M Moderate
20576 (16ande2:033) Politics (th) (th) ABC 124 dgreHldr F Liberal
20577 (16ande2:034) republican stance on deficit (pr) (pr) VRE 111 nonDgreHldr F Liberal
20578 (16ande2:034) republican stance on deficit (th) (th) ABC 111 nonDgreHldr F Liberal
20579 (16ande2:035) reverse the thanksgiving roles from a turkeys position (pr) (pr) VRE 116 nonDgreHldr F Conservative
20580 (16ande2:035) reverse the thanksgiving roles from a turkeys position (th) (th) ABC 116 nonDgreHldr F Conservative
20581 (16ande2:036) super committee (PEO) (PEO) PEO 109 dgreHldr M Moderate
20582 (16ande2:036) super committee (ss) (ss) PRA 109 dgreHldr M Moderate
20583 (16ande2:037) thanksgiving (at) (at) ABC 119 nonDgreHldr M Moderate
20584 (16ande2:037) thanksgiving (ta) (ta) C/S 119 nonDgreHldr M Moderate
20585 (16ande2:038) Thanksgiving (at) (at) ABC 117 nonDgreHldr F Conservative
20586 (16ande2:038) Thanksgiving (ta) (ta) C/S 117 nonDgreHldr F Conservative
20587 (16ande2:039) Thanksgiving (at) (at) ABC 124 dgreHldr F Liberal
20588 (16ande2:039) Thanksgiving (ta) (ta) C/S 124 dgreHldr F Liberal
20589 (16ande2:040) thanksgiving (at) (at) ABC 109 dgreHldr M Moderate
20590 (16ande2:040) thanksgiving (ta) (ta) C/S 109 dgreHldr M Moderate
20591 (16ande2:041) Thanksgiving (at) (at) ABC 121 dgreHldr M Moderate
20592 (16ande2:041) Thanksgiving (ta) (ta) C/S 121 dgreHldr M Moderate
20593 (16ande2:042) [thanksgiving turkey] deficit (ob) (ob) LOB 104 nonDgreHldr M Moderate
20594 (16ande2:042) thanksgiving turkey [deficit] (th) (th) ABC 104 nonDgreHldr M Moderate
20595 (16ande2:042) [Thanksgiving Turkey] Deficit (ob) (ob) LOB 122 dgreHldr F Liberal
20596 (16ande2:042) Thanksgiving Turkey [Deficit] (th) (th) ABC 122 dgreHldr F Liberal
20597 (16ande2:043) the [deficit] is beating up on the deficit committee (tx) (tx) LOB 103 nonDgreHldr F Moderate
20598 (16ande2:043) the [deficit] is beating up on the deficit committee (th) (th) ABC 103 nonDgreHldr F Moderate
306
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20599 (16ande2:043) the deficit is beating up on the [deficit committee] (PEO) (PEO) PEO 103 nonDgreHldr F Moderate
20600 (16ande2:043) the deficit is beating up on the [deficit committee] (ss) (ss) PRA 103 nonDgreHldr F Moderate
20601 (16ande2:043) the deficit is beating up on the deficit committee (pr) (pr) VRE 103 nonDgreHldr F Moderate
20602 (16ande2:043) the deficit is beating up on the deficit committee (th) (th) ABC 103 nonDgreHldr F Moderate
20603 (16ande2:044) Turkey (ob) (ob) LOB 114 dgreHldr F Conservative
20604 (16ande2:044) Turkey (ob) (ob) LOB 124 dgreHldr F Liberal
20605 (16ande2:045) [turkey] deficit (ob) (ob) LOB 123 nonDgreHldr M Moderate
20606 (16ande2:045) turkey [deficit] (th) (th) ABC 123 nonDgreHldr M Moderate
20607 (16ande2:046) [turkey] deficit (ob) (ob) LOB 118 nonDgreHldr F Conservative
20608 (16ande2:046) turkey [deficit] (th) (th) ABC 118 nonDgreHldr F Conservative
20609 (16ande2:047) [Uncontrollable Decific] Thanksgiving (pr) (pr) VRE 115 nonDgreHldr M Liberal
20610 (16ande2:047) [Uncontrollable Decific] Thanksgiving (th) (th) ABC 115 nonDgreHldr M Liberal
20611 (16ande2:047) Uncontrollable Decific [Thanksgiving] (at) (at) ABC 115 nonDgreHldr M Liberal
20612 (16ande2:047) Uncontrollable Decific [Thanksgiving] (pr) (pr) VRE 115 nonDgreHldr M Liberal
20613 (16ande2:048) US money issues (th) (th) ABC 117 nonDgreHldr F Conservative
20614 (17bree2:001) bob filner (pe) (pe) PEO 119 nonDgreHldr M Moderate
20615 (17bree2:001) bob filner (tx) (tx) LOB 119 nonDgreHldr M Moderate
20616 (17bree2:002) bob filner (pe) (pe) PEO 105 dgreHldr M Liberal
20617 (17bree2:002) bob filner (tx) (tx) LOB 105 dgreHldr M Liberal
20618 (17bree2:003) Bob Filner (pe) (pe) PEO 114 dgreHldr F Conservative
20619 (17bree2:003) Bob Filner (tx) (tx) LOB 114 dgreHldr F Conservative
20620 (17bree2:004) Bob Filner (pe) (pe) PEO 120 dgreHldr F Moderate
20621 (17bree2:004) Bob Filner (tx) (tx) LOB 120 dgreHldr F Moderate
20622 (17bree2:005) Bob Filner (pe) (pe) PEO 124 dgreHldr F Liberal
20623 (17bree2:005) Bob Filner (tx) (tx) LOB 124 dgreHldr F Liberal
20624 (17bree2:006) [Bob Filner] Medical Marijuana (pe) (pe) PEO 123 nonDgreHldr M Moderate
20625 (17bree2:006) [Bob Filner] Medical Marijuana (tx) (tx) LOB 123 nonDgreHldr M Moderate
20626 (17bree2:006) Bob Filner [Medical Marijuana] (ob) (ob) LOB 123 nonDgreHldr M Moderate
20627 (17bree2:006) Bob Filner [Medical Marijuana] (th) (th) ABC 115 nonDgreHldr M Liberal
20628 (17bree2:007) [bob filner] cartoon (pe) (pe) PEO 115 nonDgreHldr M Liberal
20629 (17bree2:007) [bob filner] cartoon (tx) (tx) LOB 115 nonDgreHldr M Liberal
20630 (17bree2:007) bob filner cartoon (fo) (fo) AHI 115 nonDgreHldr M Liberal
20631 (17bree2:008) Bob Fliner (pe) (pe) PEO 121 dgreHldr M Moderate
20632 (17bree2:008) Bob Fliner (tx) (tx) LOB 121 dgreHldr M Moderate
20633 (17bree2:009) Califonia (ca) (ca) C/S 121 dgreHldr M Moderate
20634 (17bree2:010) california (ca) (ca) C/S 101 nonDgreHldr F Conservative
20635 (17bree2:011) congress (ab) (ab) ABC 109 dgreHldr M Moderate
20636 (17bree2:012) Congressman (pe) (pe) PEO 114 dgreHldr F Conservative
307
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20637 (17bree2:012) Congressman (ss) (ss) PRA 114 dgreHldr F Conservative
20638 (17bree2:012) Congressman (tx) (tx) LOB 114 dgreHldr F Conservative
20639 (17bree2:013) [congressman] marijuana (pe) (pe) PEO 103 nonDgreHldr F Moderate
20640 (17bree2:013) [congressman] marijuana (tx) (tx) LOB 103 nonDgreHldr F Moderate
20641 (17bree2:013) congressman [marijuana] (ob) (ob) LOB 103 nonDgreHldr F Moderate
20642 (17bree2:013) congressman [marijuana] (th) (th) ABC 103 nonDgreHldr F Moderate
20643 (17bree2:014) [Congressman] for marijuana (pe) (pe) PEO 123 nonDgreHldr M Moderate
20644 (17bree2:014) Congressman for marijuana (pr) (pr) VRE 123 nonDgreHldr M Moderate
20645 (17bree2:014) Congressman for marijuana (th) (th) ABC 123 nonDgreHldr M Moderate
20646 (17bree2:015) controversial US issues (th) (th) ABC 117 nonDgreHldr F Conservative
20647 (17bree2:016) Deficit Turkey (WTF) (WTF) WTF 100 nonDgreHldr F Moderate
20648 (17bree2:017) Drugs (ob) (ob) LOB 124 dgreHldr F Liberal
20649 (17bree2:017) Drugs (th) (th) ABC 124 dgreHldr F Liberal
20650 (17bree2:018) [Filner] legalize (pot OR marijuana OR weed) cartoon (pe) (pe) PEO 102 dgreHldr M Liberal
20651 (17bree2:018) [Filner] legalize (pot OR marijuana OR weed) cartoon (tx) (tx) LOB 102 dgreHldr M Liberal
20652 (17bree2:018) Filner [legalize (pot OR marijuana OR weed)] cartoon (pr) (pr) VRE 102 dgreHldr M Liberal
20653 (17bree2:018) Filner [legalize (pot OR marijuana OR weed)] cartoon (th) (th) ABC 102 dgreHldr M Liberal
20654 (17bree2:018) Filner legalize (pot OR marijuana OR weed) cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20655 (17bree2:019) hippie protester (pe) (pe) PEO 107 nonDgreHldr F Moderate
20656 (17bree2:019) hippie protester (ss) (ss) PRA 107 nonDgreHldr F Moderate
20657 (17bree2:020) [hippie] vs cop jokes (pe) (pe) PEO 105 dgreHldr M Liberal
20658 (17bree2:020) [hippie] vs cop jokes (ss) (ss) PRA 105 dgreHldr M Liberal
20659 (17bree2:020) hippie vs [cop] jokes (pe) (pe) PEO 105 dgreHldr M Liberal
20660 (17bree2:020) hippie vs [cop] jokes (ss) (ss) PRA 105 dgreHldr M Liberal
20661 (17bree2:020) hippie vs cop jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20662 (17bree2:021) humor on legalizing marijuana (pr) (pr) VRE 116 nonDgreHldr F Conservative
20663 (17bree2:021) humor on legalizing marijuana (th) (th) ABC 116 nonDgreHldr F Conservative
20664 (17bree2:022) legalization of marijuana (th) (th) ABC 120 dgreHldr F Moderate
20665 (17bree2:023) legalize (tx) (tx) LOB 113 nonDgreHldr F Moderate
20666 (17bree2:024) Legalize (tx) (tx) LOB 114 dgreHldr F Conservative
20667 (17bree2:025) [legalize it] san diego union-tribune (ob) (ob) LOB 110 nonDgreHldr M Moderate
20668 (17bree2:025) legalize it [san diego union-tribune] (AHI) (AHI) AHI 110 nonDgreHldr M Moderate
20669 (17bree2:026) legalize marijuana cartoon (fo) (fo) AHI 104 nonDgreHldr M Moderate
20670 (17bree2:026) legalize marijuana cartoon (th) (th) ABC 104 nonDgreHldr M Moderate
20671 (17bree2:027) legalize pot jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20672 (17bree2:027) legalize pot jokes (th) (th) ABC 105 dgreHldr M Liberal
20673 (17bree2:028) legalize weed (th) (th) ABC 101 nonDgreHldr F Conservative
20674 (17bree2:029) legalize weed (th) (th) ABC 118 nonDgreHldr F Conservative
308
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20675 (17bree2:030) legization of marijuana (pr) (pr) VRE 111 nonDgreHldr F Liberal
20676 (17bree2:030) legization of marijuana (th) (th) ABC 111 nonDgreHldr F Liberal
20677 (17bree2:031) marijuana (ob) (ob) LOB 109 dgreHldr M Moderate
20678 (17bree2:031) marijuana (th) (th) ABC 109 dgreHldr M Moderate
20679 (17bree2:032) marijuana (ob) (ob) LOB 113 nonDgreHldr F Moderate
20680 (17bree2:032) marijuana (th) (th) ABC 113 nonDgreHldr F Moderate
20681 (17bree2:033) Marijuana (ob) (ob) LOB 114 dgreHldr F Conservative
20682 (17bree2:033) Marijuana (th) (th) ABC 114 dgreHldr F Conservative
20683 (17bree2:034) marijuana (ob) (ob) LOB 117 nonDgreHldr F Conservative
20684 (17bree2:034) marijuana (th) (th) ABC 117 nonDgreHldr F Conservative
20685 (17bree2:035) marijuana (ob) (ob) LOB 119 nonDgreHldr M Moderate
20686 (17bree2:035) marijuana (th) (th) ABC 119 nonDgreHldr M Moderate
20687 (17bree2:036) Marijuana (ob) (ob) LOB 121 dgreHldr M Moderate
20688 (17bree2:036) Marijuana (th) (th) ABC 121 dgreHldr M Moderate
20689 (17bree2:037) Marijuana (ob) (ob) LOB 124 dgreHldr F Liberal
20690 (17bree2:037) Marijuana (th) (th) ABC 124 dgreHldr F Liberal
20691 (17bree2:038) marijuana reform (th) (th) ABC 111 nonDgreHldr F Liberal
20692 (17bree2:039) NORML (tx) (tx) LOB 108 nonDgreHldr F Moderate
20693 (17bree2:040) NY protests (rf) (rf) ERE 117 nonDgreHldr F Conservative
20694 (17bree2:041) Occupy (rf) (rf) ERE 100 nonDgreHldr F Moderate
20695 (17bree2:042) occupy (rf) (rf) ERE 109 dgreHldr M Moderate
20696 (17bree2:043) [Occupy] Legalize Marijuana (rf) (rf) ERE 122 dgreHldr F Liberal
20697 (17bree2:043) Occupy [Legalize Marijuana] (pr) (pr) VRE 122 dgreHldr F Liberal
20698 (17bree2:043) Occupy [Legalize Marijuana] (th) (th) ABC 122 dgreHldr F Liberal
20699 (17bree2:044) Occupy Movement (rf) (rf) ERE 121 dgreHldr M Moderate
20700 (17bree2:045) [occupy wall street] parody (rf) (rf) ERE 105 dgreHldr M Liberal
20701 (17bree2:045) occupy wall street parody (ca) (ca) C/S 105 dgreHldr M Liberal
20702 (17bree2:046) [Occupy wall street] political cartoon (rf) (rf) ERE 112 nonDgreHldr F Moderate
20703 (17bree2:046) Occupy wall street political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20704 (17bree2:047) Occupy Wallstreet (rf) (rf) ERE 124 dgreHldr F Liberal
20705 (17bree2:048) Police (pe) (pe) PEO 100 nonDgreHldr F Moderate
20706 (17bree2:048) Police (ss) (ss) PRA 100 nonDgreHldr F Moderate
20707 (17bree2:049) political cartoon (fo) (fo) AHI 119 nonDgreHldr M Moderate
20708 (17bree2:050) Political Parties (ab) (ab) ABC 100 nonDgreHldr F Moderate
20709 (17bree2:051) political profiles (ab) (ab) ABC 108 nonDgreHldr F Moderate
20710 (17bree2:052) pot (ob) (ob) LOB 109 dgreHldr M Moderate
20711 (17bree2:052) pot (th) (th) ABC 109 dgreHldr M Moderate
20712 (17bree2:053) pot (ob) (ob) LOB 113 nonDgreHldr F Moderate
309
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20713 (17bree2:053) pot (th) (th) ABC 113 nonDgreHldr F Moderate
20714 (17bree2:054) pot (ob) (ob) LOB 119 nonDgreHldr M Moderate
20715 (17bree2:054) pot (th) (th) ABC 119 nonDgreHldr M Moderate
20716 (17bree2:055) Protest (ev) (ev) C/S 124 dgreHldr F Liberal
20717 (17bree2:056) Protestor (pe) (pe) PEO 114 dgreHldr F Conservative
20718 (17bree2:056) Protestor (ss) (ss) PRA 114 dgreHldr F Conservative
20719 (17bree2:057) Weed (ob) (ob) LOB 100 nonDgreHldr F Moderate
20720 (17bree2:057) Weed (th) (th) ABC 100 nonDgreHldr F Moderate
20721 (17bree2:058) weed in the government (pr) (pr) VRE 105 dgreHldr M Liberal
20722 (17bree2:058) weed in the government (th) (th) ABC 105 dgreHldr M Liberal
20723 (17bree2:059) [weed man bob filner] hiding from police cartoon (pe) (pe) PEO 106 nonDgreHldr F Moderate
20724 (17bree2:059) [weed man bob filner] hiding from police cartoon (de) (de) DES 106 nonDgreHldr F Moderate
20725 (17bree2:059) weed man bob filner hiding from police cartoon (fo) (fo) AHI 106 nonDgreHldr F Moderate
20726 (17bree2:059) weed man bob filner hiding from police cartoon (pr) (pr) VRE 106 nonDgreHldr F Moderate
20727 (17bree2:059) weed man bob filner hiding from police cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20728 (17bree2:060) who has the power in america? (pr) (pr) VRE 108 nonDgreHldr F Moderate
20729 (17bree2:060) who has the power in america? (th) (th) ABC 108 nonDgreHldr F Moderate
20730 (18hand2:001) Ameircan middle class (th) (th) ABC 111 nonDgreHldr F Liberal
20731 (18hand2:002) America (ab) (ab) ABC 114 dgreHldr F Conservative
20732 (18hand2:003) America (ab) (ab) ABC 124 dgreHldr F Liberal
20733 (18hand2:004) Americas Fading middle class (th) (th) ABC 118 nonDgreHldr F Conservative
20734 (18hand2:004) Americas Fading middle class (tx) (tx) LOB 118 nonDgreHldr F Conservative
20735 (18hand2:005) america's fading middle class (th) (th) ABC 101 nonDgreHldr F Conservative
20736 (18hand2:005) america's fading middle class (tx) (tx) LOB 101 nonDgreHldr F Conservative
20737 (18hand2:006) americas fading middle class cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20738 (18hand2:006) americas fading middle class cartoon (th) (th) ABC 110 nonDgreHldr M Moderate
20739 (18hand2:006) americas fading middle class cartoon (tx) (tx) LOB 110 nonDgreHldr M Moderate
20740 (18hand2:007) cartoon (fo) (fo) AHI 119 nonDgreHldr M Moderate
20741 (18hand2:008) class seperation (th) (th) ABC 124 dgreHldr F Liberal
20742 (18hand2:009) class wars (th) (th) ABC 124 dgreHldr F Liberal
20743 (18hand2:010) current state of middle class americans (pr) (pr) VRE 111 nonDgreHldr F Liberal
20744 (18hand2:010) current state of middle class americans (th) (th) ABC 111 nonDgreHldr F Liberal
20745 (18hand2:011) economy (ab) (ab) ABC 119 nonDgreHldr M Moderate
20746 (18hand2:012) economy (ab) (ab) ABC 109 dgreHldr M Moderate
20747 (18hand2:013) fade (ac) (ac) C/S 100 nonDgreHldr F Moderate
20748 (18hand2:014) Fading (ac) (ac) C/S 114 dgreHldr F Conservative
20749 (18hand2:014) Fading (tx) (tx) LOB 114 dgreHldr F Conservative
20750 (18hand2:015) [fading middle class] cartoon (tx) (tx) LOB 104 nonDgreHldr M Moderate
310
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20751 (18hand2:015) fading middle class cartoon (fo) (fo) AHI 104 nonDgreHldr M Moderate
20752 (18hand2:015) fading middle class cartoon (th) (th) ABC 104 nonDgreHldr M Moderate
20753 (18hand2:016) [fading middle class] cartoon (tx) (tx) LOB 123 nonDgreHldr M Moderate
20754 (18hand2:016) fading middle class cartoon (fo) (fo) AHI 123 nonDgreHldr M Moderate
20755 (18hand2:016) fading middle class cartoon (th) (th) ABC 123 nonDgreHldr M Moderate
20756 (18hand2:017) [fading middle class] cartoon (tx) (tx) LOB 102 dgreHldr M Liberal
20757 (18hand2:017) fading middle class cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20758 (18hand2:017) fading middle class cartoon (th) (th) ABC 102 dgreHldr M Liberal
20759 (18hand2:018) [Fading Middle Class] Diminishing (th) (th) ABC 115 nonDgreHldr M Liberal
20760 (18hand2:018) [Fading Middle Class] Diminishing (tx) (tx) LOB 115 nonDgreHldr M Liberal
20761 (18hand2:018) Fading Middle Class [Diminishing] (ac) (ac) C/S 115 nonDgreHldr M Liberal
20762 (18hand2:019) [fading middle class] invisible man (th) (th) ABC 106 nonDgreHldr F Moderate
20763 (18hand2:019) [fading middle class] invisible man (tx) (tx) LOB 106 nonDgreHldr F Moderate
20764 (18hand2:019) fading middle class [invisible man] (pe) (pe) PEO 106 nonDgreHldr F Moderate
20765 (18hand2:019) fading middle class [invisible man] (ss) (ss) PRA 106 nonDgreHldr F Moderate
20766 (18hand2:020) headline news (ob) (ob) LOB 117 nonDgreHldr F Conservative
20767 (18hand2:021) invisible man (pe) (pe) PEO 106 nonDgreHldr F Moderate
20768 (18hand2:021) invisible man (ss) (ss) PRA 106 nonDgreHldr F Moderate
20769 (18hand2:022) [invisible middle class] America (pr) (pr) VRE 108 nonDgreHldr F Moderate
20770 (18hand2:022) [invisible middle class] America (th) (th) ABC 108 nonDgreHldr F Moderate
20771 (18hand2:022) invisible middle class [America] (ab) (ab) ABC 108 nonDgreHldr F Moderate
20772 (18hand2:023) jokes on the mojority of us (pr) (pr) VRE 105 dgreHldr M Liberal
20773 (18hand2:024) lower class (th) (th) ABC 121 dgreHldr M Moderate
20774 (18hand2:025) Middle Class (th) (th) ABC 114 dgreHldr F Conservative
20775 (18hand2:025) Middle Class (tx) (tx) LOB 114 dgreHldr F Conservative
20776 (18hand2:026) middle class (th) (th) ABC 124 dgreHldr F Liberal
20777 (18hand2:026) middle class (tx) (tx) LOB 124 dgreHldr F Liberal
20778 (18hand2:027) middle class (th) (th) ABC 109 dgreHldr M Moderate
20779 (18hand2:027) middle class (tx) (tx) LOB 109 dgreHldr M Moderate
20780 (18hand2:028) [middle class] 2011 (tx) (tx) LOB 120 dgreHldr F Moderate
20781 (18hand2:028) middle class [2011] (ta) (ta) C/S 120 dgreHldr F Moderate
20782 (18hand2:029) middle class becoming invisible (pr) (pr) VRE 101 nonDgreHldr F Conservative
20783 (18hand2:029) middle class becoming invisible (th) (th) ABC 101 nonDgreHldr F Conservative
20784 (18hand2:030) [middle class] news stand (th) (th) ABC 123 nonDgreHldr M Moderate
20785 (18hand2:030) middle class [news stand] (ob) (ob) LOB 123 nonDgreHldr M Moderate
20786 (18hand2:031) middles class jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20787 (18hand2:031) middles class jokes (th) (th) ABC 105 dgreHldr M Liberal
20788 (18hand2:032) News (tx) (tx) LOB 114 dgreHldr F Conservative
311
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20789 (18hand2:033) newsstand (ob) (ob) LOB 100 nonDgreHldr F Moderate
20790 (18hand2:034) No Middle class (pr) (pr) VRE 121 dgreHldr M Moderate
20791 (18hand2:034) No Middle class (th) (th) ABC 121 dgreHldr M Moderate
20792 (18hand2:035) nobody cares about the middle class anymore (pr) (pr) VRE 116 nonDgreHldr F Conservative
20793 (18hand2:036) policies affecting middle class americans (pr) (pr) VRE 111 nonDgreHldr F Liberal
20794 (18hand2:036) policies affecting middle class americans (th) (th) ABC 111 nonDgreHldr F Liberal
20795 (18hand2:037) [Political cartoon of American middle class] and income inequality (fo) (fo) AHI 112 nonDgreHldr F Moderate
20796 (18hand2:037) [Political cartoon of American middle class] and income inequality (pr) (pr) VRE 112 nonDgreHldr F Moderate
20797 (18hand2:037) [Political cartoon of American middle class] and income inequality (th) (th) ABC 112 nonDgreHldr F Moderate
20798 (18hand2:037) Political cartoon of American middle class and [income inequality] (th) (th) ABC 112 nonDgreHldr F Moderate
20799 (18hand2:038) recent economic effects (pr) (pr) VRE 108 nonDgreHldr F Moderate
20800 (18hand2:038) recent economic effects (th) (th) ABC 108 nonDgreHldr F Moderate
20801 (18hand2:039) reduced middle class (th) (th) ABC 120 dgreHldr F Moderate
20802 (18hand2:040) satire (ca) (ca) C/S 119 nonDgreHldr M Moderate
20803 (18hand2:041) separation of economic classes (th) (th) ABC 107 nonDgreHldr F Moderate
20804 (18hand2:042) Socialism (ab) (ab) ABC 117 nonDgreHldr F Conservative
20805 (18hand2:043) the disappearance of American's middle class (pr) (pr) VRE 103 nonDgreHldr F Moderate
20806 (18hand2:043) the disappearance of American's middle class (th) (th) ABC 103 nonDgreHldr F Moderate
20807 (18hand2:044) upper class (th) (th) ABC 121 dgreHldr M Moderate
20808 (18hand2:045) US financial crisis (th) (th) ABC 117 nonDgreHldr F Conservative
20809 (18hand2:046) Vanishing Middle Class (pr) (pr) VRE 122 dgreHldr F Liberal
20810 (18hand2:046) Vanishing Middle Class (th) (th) ABC 122 dgreHldr F Liberal
20811 (18hand2:047) wealth (th) (th) ABC 113 nonDgreHldr F Moderate
20812 (18hand2:048) white middle class jokes (ca) (ca) C/S 105 dgreHldr M Liberal
20813 (18hand2:048) white middle class jokes (th) (th) ABC 105 dgreHldr M Liberal
20814 (19luck2:001) Basketball (ab) (ab) ABC 124 dgreHldr F Liberal
20815 (19luck2:002) cartoon (fo) (fo) AHI 119 nonDgreHldr M Moderate
20816 (19luck2:003) [Example of the NBA lockout] and NBA fans finding other entertainment (ev) (ev) C/S 103 nonDgreHldr F Moderate
20817 (19luck2:003) [Example of the NBA lockout] and NBA fans finding other entertainment (pr) (pr) VRE 103 nonDgreHldr F Moderate
20818 (19luck2:003) [Example of the NBA lockout] and NBA fans finding other entertainment (th) (th) ABC 103 nonDgreHldr F Moderate
20819 (19luck2:003) Example of the NBA lockout and [NBA fans] finding other entertainment] (PEO) (PEO) PEO 103 nonDgreHldr F Moderate
20820 (19luck2:003) Example of the NBA lockout and [NBA fans] finding other entertainment] (ss) (ss) PRA 103 nonDgreHldr F Moderate
20821 (19luck2:003) Example of the NBA lockout and NBA fans finding other entertainment (pr) (pr) VRE 103 nonDgreHldr F Moderate
20822 (19luck2:003) Example of the NBA lockout and NBA fans finding other entertainment (th) (th) ABC 103 nonDgreHldr F Moderate
20823 (19luck2:004) fans ignorance (th) (th) ABC 100 nonDgreHldr F Moderate
20824 (19luck2:005) im over the lockout talk (pr) (pr) VRE 105 dgreHldr M Liberal
20825 (19luck2:006) Irony (ca) (ca) C/S 105 dgreHldr M Liberal
20826 (19luck2:007) issues of nba lockout (ev) (ev) C/S 111 nonDgreHldr F Liberal
312
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20827 (19luck2:007) issues of nba lockout (pr) (pr) VRE 111 nonDgreHldr F Liberal
20828 (19luck2:007) issues of nba lockout (th) (th) ABC 111 nonDgreHldr F Liberal
20829 (19luck2:008) Lifted (ab) (ab) ABC 121 dgreHldr M Moderate
20830 (19luck2:009) Lock Out (ev) (ev) C/S 114 dgreHldr F Conservative
20831 (19luck2:009) Lock Out (th) (th) ABC 114 dgreHldr F Conservative
20832 (19luck2:010) lockout (ev) (ev) C/S 124 dgreHldr F Liberal
20833 (19luck2:011) lockout (th) (th) ABC 109 dgreHldr M Moderate
20834 (19luck2:012) [National Basketball League] 2011 (ss) (ss) PRA 120 dgreHldr F Moderate
20835 (19luck2:012) [National Basketball League] 2011 (th) (th) ABC 120 dgreHldr F Moderate
20836 (19luck2:012) National Basketball League [2011] (ta) (ta) C/S 120 dgreHldr F Moderate
20837 (19luck2:013) nba (ss) (ss) PRA 113 nonDgreHldr F Moderate
20838 (19luck2:013) nba (th) (th) ABC 113 nonDgreHldr F Moderate
20839 (19luck2:014) NBA (ss) (ss) PRA 114 dgreHldr F Conservative
20840 (19luck2:014) NBA (th) (th) ABC 114 dgreHldr F Conservative
20841 (19luck2:015) NBA (ss) (ss) PRA 124 dgreHldr F Liberal
20842 (19luck2:015) NBA (th) (th) ABC 124 dgreHldr F Liberal
20843 (19luck2:016) nba (ss) (ss) PRA 109 dgreHldr M Moderate
20844 (19luck2:016) nba (th) (th) ABC 109 dgreHldr M Moderate
20845 (19luck2:017) NBA Basketball Lock Out (ev) (ev) C/S 115 nonDgreHldr M Liberal
20846 (19luck2:017) NBA Basketball Lock Out (th) (th) ABC 115 nonDgreHldr M Liberal
20847 (19luck2:018) NBA fans (PEO) (PEO) PEO 120 dgreHldr F Moderate
20848 (19luck2:019) nba lock out (ev) (ev) C/S 101 nonDgreHldr F Conservative
20849 (19luck2:019) nba lock out (th) (th) ABC 101 nonDgreHldr F Conservative
20850 (19luck2:020) NBA Lock Out (ev) (ev) C/S 122 dgreHldr F Liberal
20851 (19luck2:020) NBA Lock Out (th) (th) ABC 122 dgreHldr F Liberal
20852 (19luck2:021) nba locked out cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20853 (19luck2:021) nba locked out cartoon (th) (th) ABC 110 nonDgreHldr M Moderate
20854 (19luck2:022) NBA locked out of house cartoon (pr) (pr) VRE 106 nonDgreHldr F Moderate
20855 (19luck2:022) NBA locked out of house cartoon (th) (th) ABC 106 nonDgreHldr F Moderate
20856 (19luck2:023) nba lockout (ev) (ev) C/S 119 nonDgreHldr M Moderate
20857 (19luck2:023) nba lockout (th) (th) ABC 119 nonDgreHldr M Moderate
20858 (19luck2:024) NBA lockout (ev) (ev) C/S 100 nonDgreHldr F Moderate
20859 (19luck2:024) NBA lockout (th) (th) ABC 100 nonDgreHldr F Moderate
20860 (19luck2:025) NBA lockout (ev) (ev) C/S 117 nonDgreHldr F Conservative
20861 (19luck2:025) NBA lockout (th) (th) ABC 117 nonDgreHldr F Conservative
20862 (19luck2:026) NBA lockout (ev) (ev) C/S 121 dgreHldr M Moderate
20863 (19luck2:026) NBA lockout (th) (th) ABC 121 dgreHldr M Moderate
20864 (19luck2:027) NBA lockout (ev) (ev) C/S 111 nonDgreHldr F Liberal
313
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20865 (19luck2:027) NBA lockout (th) (th) ABC 111 nonDgreHldr F Liberal
20866 (19luck2:028) [NBA lockout] 2011 (th) (th) ABC 120 dgreHldr F Moderate
20867 (19luck2:028) NBA lockout [2011] (ta) (ta) C/S 120 dgreHldr F Moderate
20868 (19luck2:029) [nba lockout] spoof cartoon (ev) (ev) C/S 107 nonDgreHldr F Moderate
20869 (19luck2:029) [nba lockout] spoof cartoon (th) (th) ABC 107 nonDgreHldr F Moderate
20870 (19luck2:029) nba lockout [spoof cartoon] (ca) (ca) C/S 107 nonDgreHldr F Moderate
20871 (19luck2:029) nba lockout [spoof cartoon] (fo) (fo) AHI 107 nonDgreHldr F Moderate
20872 (19luck2:030) [nba lockout] cartoon (ev) (ev) C/S 104 nonDgreHldr M Moderate
20873 (19luck2:030) [nba lockout] cartoon (th) (th) ABC 104 nonDgreHldr M Moderate
20874 (19luck2:031) nba lockout cartoon (fo) (fo) AHI 104 nonDgreHldr M Moderate
20875 (19luck2:031) nba lockout cartoon (th) (th) ABC 104 nonDgreHldr M Moderate
20876 (19luck2:032) [nba lockout] fans distracted cartoon (ev) (ev) C/S 102 dgreHldr M Liberal
20877 (19luck2:032) [nba lockout] fans distracted cartoon (th) (th) ABC 102 dgreHldr M Liberal
20878 (19luck2:032) nba lockout [fans distracted] cartoon (PEO) (PEO) PEO 102 dgreHldr M Liberal
20879 (19luck2:032) nba lockout [fans distracted] cartoon (ss) (ss) PRA 102 dgreHldr M Liberal
20880 (19luck2:032) nba lockout [fans distracted] cartoon (th) (th) ABC 102 dgreHldr M Liberal
20881 (19luck2:032) nba lockout fans distracted cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20882 (19luck2:032) nba lockout fans distracted cartoon (pr) (pr) VRE 102 dgreHldr M Liberal
20883 (19luck2:033) [NBA lockout] joke (ev) (ev) C/S 105 dgreHldr M Liberal
20884 (19luck2:033) NBA lockout joke (ca) (ca) C/S 105 dgreHldr M Liberal
20885 (19luck2:033) NBA lockout joke (th) (th) ABC 105 dgreHldr M Liberal
20886 (19luck2:034) [NBA lockout] political cartoon (ev) (ev) C/S 112 nonDgreHldr F Moderate
20887 (19luck2:034) NBA lockout political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
20888 (19luck2:034) NBA lockout political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
20889 (19luck2:035) [nba lockout] political cartoon (ev) (ev) C/S 123 nonDgreHldr M Moderate
20890 (19luck2:035) nba lockout political cartoon (fo) (fo) AHI 123 nonDgreHldr M Moderate
20891 (19luck2:035) nba lockout political cartoon (th) (th) ABC 123 nonDgreHldr M Moderate
20892 (19luck2:036) Owners (PEO) (PEO) PEO 114 dgreHldr F Conservative
20893 (19luck2:036) Owners (ss) (ss) PRA 114 dgreHldr F Conservative
20894 (19luck2:037) [owners] locked out (PEO) (PEO) PEO 105 dgreHldr M Liberal
20895 (19luck2:037) [owners] locked out (ss) (ss) PRA 105 dgreHldr M Liberal
20896 (19luck2:037) owners locked out (pr) (pr) VRE 105 dgreHldr M Liberal
20897 (19luck2:037) owners locked out (th) (th) ABC 105 dgreHldr M Liberal
20898 (19luck2:038) [Owners] versus Players (PEO) (PEO) PEO 121 dgreHldr M Moderate
20899 (19luck2:038) [Owners] versus Players (ss) (ss) PRA 121 dgreHldr M Moderate
20900 (19luck2:038) Owners versus [Players] (PEO) (PEO) PEO 121 dgreHldr M Moderate
20901 (19luck2:038) Owners versus [Players] (ss) (ss) PRA 121 dgreHldr M Moderate
20902 (19luck2:038) Owners versus Players (pr) (pr) VRE 121 dgreHldr M Moderate
314
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20903 (19luck2:038) Owners versus Players (th) (th) ABC 121 dgreHldr M Moderate
20904 (19luck2:039) People who are moving on from basketball drama (pr) (pr) VRE 116 nonDgreHldr F Conservative
20905 (19luck2:039) People who are moving on from basketball drama (th) (th) ABC 116 nonDgreHldr F Conservative
20906 (19luck2:040) Players (PEO) (PEO) PEO 114 dgreHldr F Conservative
20907 (19luck2:040) Players (ss) (ss) PRA 114 dgreHldr F Conservative
20908 (19luck2:041) real housewives (rf) (rf) ERE 117 nonDgreHldr F Conservative
20909 (19luck2:041) real housewives (tx) (tx) LOB 117 nonDgreHldr F Conservative
20910 (19luck2:042) [real housewives] lockout (rf) (rf) ERE 123 nonDgreHldr M Moderate
20911 (19luck2:042) [real housewives] lockout (tx) (tx) LOB 123 nonDgreHldr M Moderate
20912 (19luck2:042) real housewives [lockout] (ev) (ev) C/S 123 nonDgreHldr M Moderate
20913 (19luck2:042) real housewives [lockout] (th) (th) ABC 123 nonDgreHldr M Moderate
20914 (19luck2:043) [Real houswives] NBA (rf) (rf) ERE 118 nonDgreHldr F Conservative
20915 (19luck2:043) [Real houswives] NBA (tx) (tx) LOB 118 nonDgreHldr F Conservative
20916 (19luck2:043) Real houswives [NBA] (ss) (ss) PRA 118 nonDgreHldr F Conservative
20917 (19luck2:043) Real houswives [NBA] (th) (th) ABC 118 nonDgreHldr F Conservative
20918 (19luck2:044) real houswives of atlanta (rf) (rf) ERE 119 nonDgreHldr M Moderate
20919 (19luck2:044) real houswives of atlanta (tx) (tx) LOB 119 nonDgreHldr M Moderate
20920 (19luck2:045) reality TV taking over America (pr) (pr) VRE 108 nonDgreHldr F Moderate
20921 (19luck2:045) reality TV taking over America (th) (th) ABC 108 nonDgreHldr F Moderate
20922 (19luck2:046) sports (pr) (pr) VRE 109 dgreHldr M Moderate
20923 (19luck2:046) sports (th) (th) ABC 109 dgreHldr M Moderate
20924 (19luck2:047) sports cartoons (fo) (fo) AHI 117 nonDgreHldr F Conservative
20925 (19luck2:047) sports cartoons (th) (th) ABC 117 nonDgreHldr F Conservative
20926 (19luck2:048) superficial america (th) (th) ABC 108 nonDgreHldr F Moderate
20927 (19luck2:049) union (ss) (ss) PRA 109 dgreHldr M Moderate
20928 (19luck2:049) union (th) (th) ABC 109 dgreHldr M Moderate
20929 (20rame2:001) a [separate water fountain] for conservative blacks (de) (de) DES 103 nonDgreHldr F Moderate
20930 (20rame2:001) a [separate water fountain] for conservative blacks (ob) (ob) LOB 103 nonDgreHldr F Moderate
20931 (20rame2:001) a separate water fountain for [conservative blacks] (ss) (ss) PRA 103 nonDgreHldr F Moderate
20932 (20rame2:001) a separate water fountain for [conservative blacks] (th) (th) ABC 103 nonDgreHldr F Moderate
20933 (20rame2:001) a separate water fountain for [conservative blacks] (tx) (tx) LOB 103 nonDgreHldr F Moderate
20934 (20rame2:001) a separate water fountain for conservative blacks (pr) (pr) VRE 103 nonDgreHldr F Moderate
20935 (20rame2:001) a separate water fountain for conservative blacks (th) (th) ABC 103 nonDgreHldr F Moderate
20936 (20rame2:002) black joke (ca) (ca) C/S 105 dgreHldr M Liberal
20937 (20rame2:002) black joke (th) (th) ABC 105 dgreHldr M Liberal
20938 (20rame2:003) black Republicans (ss) (ss) PRA 124 dgreHldr F Liberal
20939 (20rame2:003) black Republicans (th) (th) ABC 124 dgreHldr F Liberal
20940 (20rame2:004) Black stereotypes (pr) (pr) VRE 108 nonDgreHldr F Moderate
315
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20941 (20rame2:004) Black stereotypes (th) (th) ABC 108 nonDgreHldr F Moderate
20942 (20rame2:005) Blacks (ss) (ss) PRA 114 dgreHldr F Conservative
20943 (20rame2:005) Blacks (th) (th) ABC 114 dgreHldr F Conservative
20944 (20rame2:006) [blacks] split votes (ss) (ss) PRA 108 nonDgreHldr F Moderate
20945 (20rame2:006) [blacks] split votes (th) (th) ABC 108 nonDgreHldr F Moderate
20946 (20rame2:006) blacks split votes (pr) (pr) VRE 108 nonDgreHldr F Moderate
20947 (20rame2:006) blacks split votes (th) (th) ABC 108 nonDgreHldr F Moderate
20948 (20rame2:007) [blacks] using white sick cartoon (ss) (ss) PRA 110 nonDgreHldr M Moderate
20949 (20rame2:007) [blacks] using white sick cartoon (th) (th) ABC 110 nonDgreHldr M Moderate
20950 (20rame2:007) [blacks using white] sick cartoon (ab) (ab) ABC 110 nonDgreHldr M Moderate
20951 (20rame2:007) [blacks using white] sick cartoon (th) (th) ABC 110 nonDgreHldr M Moderate
20952 (20rame2:007) blacks using white sick cartoon (fo) (fo) AHI 110 nonDgreHldr M Moderate
20953 (20rame2:007) blacks using white sick cartoon (pr) (pr) VRE 110 nonDgreHldr M Moderate
20954 (20rame2:008) cartoon toilet (fo) (fo) AHI 119 nonDgreHldr M Moderate
20955 (20rame2:008) cartoon toilet (se) (se) C/S 119 nonDgreHldr M Moderate
20956 (20rame2:009) cheap (at) (at) ABC 101 nonDgreHldr F Conservative
20957 (20rame2:009) cheap (de) (de) DES 101 nonDgreHldr F Conservative
20958 (20rame2:010) conservative (ss) (ss) PRA 100 nonDgreHldr F Moderate
20959 (20rame2:010) conservative (th) (th) ABC 100 nonDgreHldr F Moderate
20960 (20rame2:011) Conservative (ss) (ss) PRA 114 dgreHldr F Conservative
20961 (20rame2:011) Conservative (th) (th) ABC 114 dgreHldr F Conservative
20962 (20rame2:012) conservative black minority (ss) (ss) PRA 104 nonDgreHldr M Moderate
20963 (20rame2:012) conservative black minority (th) (th) ABC 104 nonDgreHldr M Moderate
20964 (20rame2:013) [conservative black] segregation (ss) (ss) PRA 122 dgreHldr F Liberal
20965 (20rame2:013) [conservative black] segregation (th) (th) ABC 122 dgreHldr F Liberal
20966 (20rame2:013) conservative black [segregation] (th) (th) ABC 122 dgreHldr F Liberal
20967 (20rame2:014) [conservative black] water fountain (ss) (ss) PRA 123 nonDgreHldr M Moderate
20968 (20rame2:014) [conservative black] water fountain (th) (th) ABC 123 nonDgreHldr M Moderate
20969 (20rame2:014) conservative black [water fountain] (ob) (ob) LOB 123 nonDgreHldr M Moderate
20970 (20rame2:015) conservative blacks (ss) (ss) PRA 101 nonDgreHldr F Conservative
20971 (20rame2:015) conservative blacks (th) (th) ABC 101 nonDgreHldr F Conservative
20972 (20rame2:015) conservative blacks (tx) (tx) LOB 101 nonDgreHldr F Conservative
20973 (20rame2:016) [conservative blacks] (washroom OR bathroom OR sink) discrimination cartoon (ss) (ss) PRA 102 dgreHldr M Liberal
20974 (20rame2:016) [conservative blacks] (washroom OR bathroom OR sink) discrimination cartoon (th) (th) ABC 102 dgreHldr M Liberal
20975 (20rame2:016) [conservative blacks] (washroom OR bathroom OR sink) discrimination cartoon (tx) (tx) LOB 102 dgreHldr M Liberal
20976 (20rame2:016) conservative blacks [(washroom OR bathroom OR sink)] discrimination cartoon (ob) (ob) LOB 102 dgreHldr M Liberal
20977 (20rame2:016) conservative blacks [(washroom OR bathroom OR sink)] discrimination cartoon (se) (se) C/S 102 dgreHldr M Liberal
20978 (20rame2:016) conservative blacks (washroom OR bathroom OR sink) [discrimination] cartoon (th) (th) ABC 102 dgreHldr M Liberal
316
Table 34 - continued
PK terms attrib Class p id edu type gen politics
20979 (20rame2:016) conservative blacks (washroom OR bathroom OR sink) discrimination cartoon (fo) (fo) AHI 102 dgreHldr M Liberal
20980 (20rame2:016) conservative blacks (washroom OR bathroom OR sink) discrimination cartoon (pr) (pr) VRE 102 dgreHldr M Liberal
20981 (20rame2:017) [conservative blacks] bathroom (ss) (ss) PRA 118 nonDgreHldr F Conservative
20982 (20rame2:017) [conservative blacks] bathroom (th) (th) ABC 118 nonDgreHldr F Conservative
20983 (20rame2:017) [conservative blacks] bathroom (tx) (tx) LOB 118 nonDgreHldr F Conservative
20984 (20rame2:017) conservative blacks [bathroom] (se) (se) C/S 118 nonDgreHldr F Conservative
20985 (20rame2:018) Conservative policies (th) (th) ABC 111 nonDgreHldr F Liberal
20986 (20rame2:019) conservative republicans (ss) (ss) PRA 120 dgreHldr F Moderate
20987 (20rame2:019) conservative republicans (th) (th) ABC 120 dgreHldr F Moderate
20988 (20rame2:020) conservatives (ss) (ss) PRA 121 dgreHldr M Moderate
20989 (20rame2:020) conservatives (th) (th) ABC 121 dgreHldr M Moderate
20990 (20rame2:021) conservatives (ss) (ss) PRA 109 dgreHldr M Moderate
20991 (20rame2:021) conservatives (th) (th) ABC 109 dgreHldr M Moderate
20992 (20rame2:022) getting by with what is only necessary (pr) (pr) VRE 116 nonDgreHldr F Conservative
20993 (20rame2:023) liberals (ss) (ss) PRA 121 dgreHldr M Moderate
20994 (20rame2:023) liberals (th) (th) ABC 121 dgreHldr M Moderate
20995 (20rame2:024) minorities (ss) (ss) PRA 124 dgreHldr F Liberal
20996 (20rame2:024) minorities (th) (th) ABC 124 dgreHldr F Liberal
20997 (20rame2:025) Obama (WTF) (WTF) WTF 121 dgreHldr M Moderate
20998 (20rame2:026) Politcal Racial Segregation (pr) (pr) VRE 115 nonDgreHldr M Liberal
20999 (20rame2:026) Politcal Racial Segregation (th) (th) ABC 115 nonDgreHldr M Liberal
21000 (20rame2:027) politics (th) (th) ABC 121 dgreHldr M Moderate
21001 (20rame2:028) race (th) (th) ABC 109 dgreHldr M Moderate
21002 (20rame2:029) [racial] conservative political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
21003 (20rame2:029) racial [conservative] political cartoon (ss) (ss) PRA 112 nonDgreHldr F Moderate
21004 (20rame2:029) racial [conservative] political cartoon (th) (th) ABC 112 nonDgreHldr F Moderate
21005 (20rame2:029) racial conservative political cartoon (fo) (fo) AHI 112 nonDgreHldr F Moderate
21006 (20rame2:029) racial conservative political cartoon (pr) (pr) VRE 112 nonDgreHldr F Moderate
21007 (20rame2:030) racial political joke (ca) (ca) C/S 105 dgreHldr M Liberal
21008 (20rame2:030) racial political joke (th) (th) ABC 105 dgreHldr M Liberal
21009 (20rame2:031) racism (th) (th) ABC 119 nonDgreHldr M Moderate
21010 (20rame2:032) racism (th) (th) ABC 113 nonDgreHldr F Moderate
21011 (20rame2:033) racism (th) (th) ABC 117 nonDgreHldr F Conservative
21012 (20rame2:034) [racist] water fountain (th) (th) ABC 104 nonDgreHldr M Moderate
21013 (20rame2:034) racist [water fountain] (ob) (ob) LOB 104 nonDgreHldr M Moderate
21014 (20rame2:035) [right wing] black joke (ss) (ss) PRA 105 dgreHldr M Liberal
21015 (20rame2:035) [right wing] black joke (th) (th) ABC 105 dgreHldr M Liberal
21016 (20rame2:035) right wing black joke (ca) (ca) C/S 105 dgreHldr M Liberal
317
Table 34 - continued
PK terms attrib Class p id edu type gen politics
21017 (20rame2:035) right wing black joke (pr) (pr) VRE 105 dgreHldr M Liberal
21018 (20rame2:036) segregation (th) (th) ABC 113 nonDgreHldr F Moderate
21019 (20rame2:037) segregation (th) (th) ABC 100 nonDgreHldr F Moderate
21020 (20rame2:038) segregation (th) (th) ABC 107 nonDgreHldr F Moderate
21021 (20rame2:039) segregation (th) (th) ABC 120 dgreHldr F Moderate
21022 (20rame2:040) segregation (th) (th) ABC 124 dgreHldr F Liberal
21023 (20rame2:041) stark satire (ca) (ca) C/S 119 nonDgreHldr M Moderate
21024 (20rame2:042) [unequal water fountatins] black conservate (de) (de) DES 106 nonDgreHldr F Moderate
21025 (20rame2:042) [unequal water fountatins] black conservate (ob) (ob) LOB 106 nonDgreHldr F Moderate
21026 (20rame2:042) unequal water fountatins [black conservate] (ss) (ss) PRA 106 nonDgreHldr F Moderate
21027 (20rame2:042) unequal water fountatins [black conservate] (th) (th) ABC 106 nonDgreHldr F Moderate
21028 (20rame2:043) Water Fountain (ob) (ob) LOB 114 dgreHldr F Conservative
21029 (20rame2:044) water fountain joke (ca) (ca) C/S 105 dgreHldr M Liberal
21030 (20rame2:044) water fountain joke (ob) (ob) LOB 105 dgreHldr M Liberal
21031 (20rame2:045) [water fountain] segregation (ob) (ob) LOB 123 nonDgreHldr M Moderate
21032 (20rame2:045) water fountain [segregation] (th) (th) ABC 123 nonDgreHldr M Moderate
21033 (20rame2:046) water fountains (ob) (ob) LOB 117 nonDgreHldr F Conservative
318
APPENDIX K
INTERVIEW SCRIPT
[Participants in this phase of the study will already have Jörgensen’s 12 Classes and will
have had the opportunity to put those classes in the order that they think is most important.]
“Good morning/afternoon, Mr./Ms.____________________. Thanks very much for
taking the time out of your day for this interview. Are you ready?
[If yes, continue. If no, arrange another interview time.]
“May I record this interview?”
[If yes, continue. If no, call back using regular phone service.]
“This next part I have to read to you because of University rules. Ready?”
“Thanks very much for helping me with my research.
“I am Chris Landbeck, a doctoral candidate at Florida State University, and
I’mconducting telephone interviews to assess the usefulness of the findings of my research into
editorial cartoons, and whether these findings mirror what is known in the field. It is as an
interviewee that your help is being sought.
“The previous two parts of this three-part study gathered information about the
description of editorial cartoons and about queries for such images. The data generated in these
activities was analyzed using Jörgensen’s 12 Classes of image description to see how editorial
cartoons compared to other kinds of images in the terms used to describe them. As part of this
interview, you have been given these 12 Classes and asked to rank them in terms of importance
for describing editorial cartoons. During the interview, your predictions will be compared to the
actual results, and we will discuss these – and anything else you deem important to the
conversation – until we are satisfied that we’ve covered everything.
“This research project has been approved by and has the full support of Florida State
University.
“The interview itself will be conducted as follows: having already contacted you to
arrange this interview and sending you the 12 Classes, I have called you via a recording service
called recordmycalls.com, which allows the interview to be recorded via the Web. After reading
the require informed consent document to you, I will ask for your consent to be interviewed, and
after it is secured the interview will begin. It is estimated that the interview will take 20-30
319
minutes, and will center on whether the use of the 12 Classes is appropriate for editorial carton,
and whether the findings of the research matter to you.
“Your participation is voluntary, and you are free to decline. If you choose not to
participate or to withdraw from the study at any time, there will be no penalty. The results of the
research study may be published, but your name will not be used. The research report will be
made available to any participant who would like to see it.
“Confidentiality will be maintained to the extent allowed by law. Identifying information
will be maintained by the researchers in a locked file. Digital recordings will be stored by the
researchers on a password protected laptop. All paper and electronic files related to this research
project will be destroyed no later than two years from the date of this project (September 15,
2013).
“There are no foreseeable risks or discomforts related to your participation and the results
of the research promise to library and information studies, history and political science, art
history, and the cartooning profession.
“Please note that if at any time you have any questions about your rights as a
subject/participant in this research, or if you feel you have been placed at risk, you can contact
the Chair of the Human Subjects Committee, Institutional Review Board, through the Vice
President for the Office of Research at (850) 644-8633.
“If at any time you have any questions about this research or your participation in it,
please contact:Chris Landbeck, School of Library & Information Studies, Florida State
University at [email protected].
“Do I have your consent to proceed with this interview as outlined?”
[If yes, continue. If no, thanks the person for their time, and end the discussion.]
“Have you had a chance to put those classes of image description in order?”
[If no, allow some time for the order to be made right then.]
[Assuming an affirmative response…]
“Wonderful! What order do you have them in, please?”
[Write down the interviewee’s order for later reference]
“Why this order? What prompted you to, for instance, put the first one first?”
[Await reply]
“And why are the ones at the bottom less important?
320
[Await reply]
“Mr./Ms.____________________, I have here the order of those classes as discovered in
my research.”
[List classes]
“Does this surprise you much? Why?”
[Await response]
“Do you think that any of this might change the way you do your own work? Why?”
[Await answer]
From here, the interview will be allowed to cover whatever topics or aspects of the
research that is deemed desirable by both the researcher and the interviewee.
“Thanks very much for speaking with me today. One last thing, is there anyone else you
can think of that might want to participate in my research as you have today?”
[If yes, get contact information.]
“Would you like to see the results of his research?”
[Make note of answer.]
“OK, thanks again for your time.”
[End interview]
321
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BIOGRAPHICAL SKETCH
My research interests center on discovering how to best provide access to political
cartoons in specific, and to all images in general. I’ve taught 11 large
undergraduate classes as well as some smaller ones, and have served on several
committees for ASIS&T and the School of Library and Information Studies.
EDUCATION
Ph.D. Candidate, Florida State University, School of Library and Information Studies. Proposed
dissertation title: User Descriptions of Political Cartoons. Committee members: Dr. Corinne
Jörgensen (Chair); Drs. Michelle Kazmer, Paul Marty, and Besiki Stvilia (members); Dr. Lois
Hawkes (outside member). Expected graduation date: Spring 2013.
M.S. Library and Information Studies, Florida State University, School of Information Studies,
2002. Major: Information Studies. Master’s Thesis: The Organization and Categorization of Political Cartoons: an Exploratory Study.
B.S. Information Studies, Florida State University, School of Information Studies, 1999.
Concentration in information organization.
B.S. History, Towson State University, History Department, 1993. Concentration in American
History.
PUBLICATIONS
Book Chapter – Refereed
Landbeck, C. (2012). Access to editorial cartoons: The state of the art. In Indexing and retrieval
of non-text information. Germany: De Gruyter Saur.
Proceedings – Refereed
Landbeck, C. (2008). Issues in subject analysis and description of political cartoons. In Lussky,
J. (Ed). Proceedings 19th Workshop of the American Society for Information Science and
Technology Special Interest Group in Classification Research, Columbus, Ohio.
Journal article – invited
Landbeck, C. (2007). Trouble in Paradise: Conflict management and resolution in social
classification environments. Bulletin of the American Society of Information Science and
Technology, 34(1), 16-20.
333
Book review
Landbeck, C. (2009). Book Review. [review of the book Structures of Image Collections: From
Chauvet-Pont-d'Arc to Flickr]. Library Quarterly, 79(3), 384-387.
PRESENTATIONS
Speaker
Landbeck, C. (2012). Speaker. Describing Political Cartoons for Preservation and Access. Panel:
Preserving Imaged-Based Cultural Heritage: Valuation, Negation, or Desertion. Annual Meeting
of the American Society of Information Science and Technology (2012, Baltimore). October 28,
2012.
Landbeck, C. (2012). Guest lecturer. LIS5916 – Graphic Novels: Indexing Editorial Cartoons.
School of Library and Information Studies, Florida State University. June 4, 2012.
Landbeck, C. (2011). Speaker. Problems with Indexing Editorial Cartoons. Workshop: Hands On
with the State of the Art (SIG VIS). Annual Meeting of the American Society of Information
Science and Technology (2011, New Orleans). October 12, 2010.
Landbeck, C. (2012). Guest lecturer. Proseminar: The Doctoral Experience. School of Library
and Information Studies, Florida State University. March 28, 2012.
Landbeck, C. (2011). Guest lecturer. Proseminar: The Dissertation Experience. School of Library
and Information Studies, Florida State University. November 21, 2011.
Landbeck, C., et al. (2010). Guest lecturer. Proseminar: Student Opportunities in LIS
Organizations. School of Library and Information Studies, Florida State University. January 20,
September 9, and November 10, 2010, and September 20, 2011.
Landbeck, C. (2008). Colloquium Presentation. The Nature of Information: What neither Mozart
nor Dilbert could tell us. School of Library and Information Studies, Florida State University.
April 2, 2008.
Swain, D. E., Pulliam, B., Liberman, K., Neal, D., Landbeck, C., Edwards, P. M., et al. (2008).
Speed Meeting: A special session to introduce attendees to each other in person and via Web
cast. Proceedings of the American Society for Information Science and Technology, 45, 1-2.
Moderator
Landbeck, C., et al. (2011). Moderator. Workshop: Hands On with the State of the Art (SIG
VIS). Annual Meeting of the American Society of Information Science and Technology (2011,
New Orleans). October 12, 2011.
334
Landbeck, C., et al. (2010). Moderator. Workshop: Current Research and Thinking in Image
Analysis, Descriptions, and Systems (SIG VIS). Annual Meeting of the American Society of
Information Science and Technology (2010, Pittsburgh). October 27, 2010.
Posters
Landbeck, C. (2012). Poster. Describing Political Cartoons: Jörgensen’s 12 Classes. Association
of Library and Information Science Educators. Dallas, TX. January 18, 2012.
Landbeck, C. (2010). Poster. The Nature of Information: A Novel Approach Comparing
Undergrads and Academics. Association of Library and Information Science Educators. Boston,
MA. January 14, 2010.
Landbeck, C. (2009). Poster. Computing Careers Outreach. STARS Alliance Conference.
Tallahassee, FL. August 10, 2009.
Landbeck, C. (2006). Poster. Methods of Fighting Madness: Conflict Resolution in Social
Classification Environments. SIG CR workshop: Social Classification; ASIS&T. November 3,
2006.
RESEARCH EXPERIENCE
Research Collaboration with Dr. Michelle Kazmer, School of Library and Information Studies,
Florida State University. 2008-2010. Designed research model, conducted research, and analyzed
data from student essays about the nature of information.
Research Collaboration with Dr. Michelle Kazmer, School of Library and Information Studies,
Florida State University. 2007-2008. Composed end-of-grant final report for Librarians Serving
the Public, an IMLS grant.
Research Collaboration with Dr. Corinne Jörgensen, School of Library and Information Studies,
Florida State University, 2006-2007. Amended nascent visual thesaurus as part of an OCLC
grant, designed and tested survey instrument, gathered and analyzed survey data.
TEACHING
Florida State University, School of Library and Information Studies
Instructor
LIS3201 – Data Collection and Analysis: 2007-2011, 2013. Foundation class for the Information
Technology undergraduate major. Introduced undergraduates to the concepts of quantitative and
qualitative data, various data collection methods and the role of Information Technology in
business.
335
LIS4941 – Practicum: Spring 2007. Volunteered to lead a group of undergraduate students
through the process of improving an existing website, with a client and audience analysis, site
development, and usability tests.
LIS 3267 – Information Science for Information Professionals: 2006-2007, 2009-2011.
Foundation class for the Information Technology undergraduate major. Introduced the history of
information science, explored ethical and philosophical issues in the Information Age, user-
centered design concepts such as audience analysis, and interface design.
LIS 3784 – Information Organization: Summer 2006. Intermediate class for the Information
Technology undergraduate major. Introduced and explored ideas of surrogation, aggregation,
taxonomy, ontology, and tagging, and the history of information organization in the modern age.
Teaching Assistant
LIS3706 – Information Systems and Services, Spring 2012. Assisted in development and
deployment of new, visualization-oriented course for undergraduates. Taught lab section,
clarifying concepts and assisting students with assignments and projects.
LIS4941 – Practicum: Spring 2011. Assisted in service-learning course grading and guidance.
Allowed instructor to focus on core competencies in students’ work experiences, and administered the course’s Blackboard 9.0 site.
LIS4910 – Capstone (Project): Summer 2009, 2011, 2012. A post-requisite class for the
Information Technology undergraduate major. Instructed students in the proper creation and
execution of project plans, including the documentation of activities and professional conduct.
LIS5271 – Research Methods: 2007-2008. Assisted in teaching a foundation class for the
graduate school. Introduced students to the practice of research in academic settings, ethical
concerns in research, and proper conduct of several types of research.
LIS3946 – Field Study: Spring, 2006. Led group of undergraduate students through the process
of improving an existing website, starting with a client and audience analysis, site development,
usability tests, and documentation.
LIS 3267 – Information Science for Information Professionals: 2005-2006, 2012. Foundation
class for the Information Technology undergraduate major. Administered class as a liaison
between students and professor, evaluated work of students, mediated disputes in student groups,
and introduced students to concepts in information organization and usability.
SERVICE
American Society for Information Science and Technology (ASIS&T) – general body
Chair, SIG Cabinet, 2012-present.
336
Deputy Chair, SIG Cabinet, 2011-2012.
Member, New Leaders Award Committee, 2011.
Reviewer, Annual Meeting, 2009, 2011, 2012.
Member, Nominations Committee, 2010-present.
Member, Membership Committee (Watson Davis Award Jury), 2009-present.
Member, SIG Cabinet Steering Committee, 2008-2011.
Student Volunteer, ASIS&T Conference (Milwaukee), 2007.
Special Interest Group for Visualization, Images, and Sound (SIGVIS) – ASIS&T
Past Chair, 2011-present.
Workshop Director, 2010-2011.
Chair, 2008-2011.
Vice-Chair, 2007-2008.
Webmaster, 2006-2008.
Association for Library and Information Science Education (ALISE)
Reviewer, Journal for Education in Library and Information Science (JELIS), 2010.
Students and Technology in Academia, Research, and Service (STARS)
Senior Coordinator, 2009. General meeting, STARS Alliance Conference.
President, Florida State University Student Leadership Corps, STARS, 2008-2009.
Vice-President, Florida State University Student Leadership Corps, STARS, 2007.
Florida State University – School of Library and Information Studies/School of Library and
Information Studies
Undergraduate Steering Committee, 2010-present.
New Doctoral Student Orientation leader, 2007-present.
Doctoral Planning Team, 2007-2008 & 2010-2011.
PROFESSIONAL MEMBERSHIP
Association for Library Science and Education, 2009-present
Visual Resource Association (VRA), 2008-present
American Society of Information Science and Technology (ASIS&T), 2006-present
Special Interest Groups:
Classification Research (CR)
History and Foundations of Information Science (HFIS)
Visualization, Images, and Sound (VIS)
337
HONORS
John M. Goudeau Scholarship, 2008: For doctoral students specializing in academic
librarianship.
Lewis-Marksen Fellowship, 2007: An annual fellowship for a student working on an advanced
degree.
H.W. Wilson Foundation Scholarship, 2006: Provides to aid students with exceptional academic
records and to attract potential information professionals to the field.
College Teaching Fellowship, 2005: Provides support to students who wish to serve as a
Teaching Assistant during their first year in the Doctoral program.
PROFESSIONAL EXPERIENCE
Consultant, Electronic Document Management System, Florida Department of Transportation,
2001-2002. Managed implementation of new software while maintaining file integrity for FDOT
records.
Help Desk Analyst, Flowers Inc., 2001. Managed help desk and networks for Fortune 500
company.
Researcher, Claude Pepper Library, Florida State University, 2000-2001. Investigated methods
of classifying and cataloging political cartoons of the late Claude Pepper, former U.S. Senator
and Representative, by subject and related news items.
Soldier, Infantry, United States Army (Active and National Guard), 1988-1999. Promoted to
Sergeant (E-5), head of anti-armor section, awarded both Expert Infantry Badge and Army
Commendation Medal.
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