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Twitter AnalyticsA user’s guide to interpreting, reinterpreting and misinterpreting the social media service.
Dr Stephen DannSchool of Management
Marketing & International Business, Australian National University
@stephendann or stephen.dann@anu.edu.au
If you’re on Twitter
Questions can be sent to @stephendann
Twitter. Twitter matters because of what it is: at its heart, a platform that offers an exchange of ideas and information on an unprecedented scale.Why Twitter Matters : Marketing : Idea Hub :: American Express OPEN Forumhttp://www.openforum.com/idea-hub/topics/marketing/article/why-twitter-matters-ann-handleyFri Oct 02 2009 21:16:49 GMT+1000 (AUS Eastern Standard Time)
Twitter in Plain English
For those who came in lateTwitter.com• 140 character message• Social network• Web2.0• End of the world as we know it• Best thing since sliced bread
Twitter! (What is it good for?)
• health community (Berger 2009)• public libraries (Cahill 2009, Cuddy 2009)• political campaigns (Cetina 2009, Henneburg et al
2009)• business (Dudley 2009; Power and Forte 2008)• journalism (Ettama 2009)• civil unrest and protests (Fahmi 2009)• social activism (Galer-Unti 2009)• live coverage of events (Gay et al 2009)• eyewitness accounts (Lariscy et al 2009)• government (Macintosh 2009)• education (Parslow 2009).
Uses and usage• casual listening platform (Crawford 2009), • creating the illusion of physicality (Hohl
2009) • sense of connectedness and relationship
(Henneburg et al 2009)• venue for conversation (Steiner 2009)
How to dissect a living medium?
Raw Counts
Tweetstats – www.tweetstats.com
Text Analysis
Tweetstats – www.tweetstats.com Wordle – wordle.com
Leximancer
Leximancer – www.leximancer.com
Coded Content Analysis
Made up for this set of slides.
Same Data set…
So many different ways to present the results
Coded Content Analysis
Social awareness streamsThree factors 1. the public (or personal-public) nature of
the communication and conversation2. the brevity of posted content3. highly connected social space /
articulated online contact networks.
Naaman, Boase, and Lai (2010),
infolab.stanford.edu/~mor/research/naamanCSCW10.pdf
Prior Analysis
Analysis 1: Take the people out
Krishnamurthy et al (2008) •users were classified by
–follower/following counts,
•Numbers and ratios–means and mechanisms of their engagement
•Web (61.7%), mobile/text (7.5%), software (22.4%)
–volume of use •Tweets per time period
Analysis 2: Content Category
Java et al 2007• 1,348,543 tweets• 76,177 users • April 01, to May 30, 2007
Four meta-categories • daily chatter• conversations• information / URL sharing• news reporting
Analysis 3: Insider CodingJansen et al (2009) • tweets with brand name • expression of brand sentiment
• 13-week period–April 4, 2008 to July 3, 2008.
•650 reporting episodes –13 x 50 brands
•149,472 tweets
Sentiment Scale• No Sentiment• Wretched• Bad• So-so• Swell• Great
Content Schema• Sentiment• Information seeking• Information providing • Comment
Analysis 4: Pear’s BabblePear Analytics (2009)• 2000 tweets• 11am to 5pm• 10 working days
Six part classification• news (3.6%), • spam (3.75%), • self-promotion (5.85%), • pointless babble (40.55%)• conversational (37.55%)• pass-along value (8.70%).
Analysis 5: Where’s the party @?
Honeycutt and Herring (2009)• four one-hour samples • four-hour intervals• 6 a.m. to 6 p.m. Eastern Standard Time, on January 11, 2008
•Sample of 200 tweets coded with grounded methodology
1) Addressivity: Directs a message to another person2) Reference: Makes reference to another person, butdoes not direct a message to him or her. 3) Emoticon: Used as part of an emoticon. 4) Email: Used as part of an email address. 5) Locational 'at': Signals where an entity is located.6) Non-locational 'at': Used to represent the preposition 'at' other than in the sense of location. 7) Other: Uses not fitting into any other category,
Analysis 6: Rigor and BassNaaman, Boase and Lai (2010)• Sample of 400 tweets
–more than one category was assigned to a single message.
• Sampling frame –125,593 unique user IDs –‘personal’ Twitter users–10 friends, 10 followers, 10 messages–911 users
•N = 350 users
The Categories• Information Sharing• Self Promotion• Opinions/Complaints• Statements and Random Thoughts• Me now• Question to followers• Presence Maintenance• Anecdote (me)• Anecdote (others)
The consistent themePeople keep using Twitter for personal use.
• Discussions of “self”• Pointless babble • Conversational
All criticisms of the use of twitter for pleasure and personal consumption
What Twitter looks like…
…and how are people using Twitter?
Twitter – www.twitter.com
Recoding the Platform
Let’s do it my way
Theory and IdeologyUseful versus Enjoyable
Bohme (2006) outlines a propensity of society to classify technology of all forms into – “useful and therefore valuable” – “enjoyable, therefore irrelevant”.
Böhme, G (2006) Technical Gadgetry: Technological Development in the Aesthetic Economy, Thesis Eleven, 86 (1): 54-66
MethodGrounded Theory
• Broad categories based on / supported by six prior studies
• Sub categories developed from theory and data
SamplePersonal Twitter History@stephendann (274 Following / 355
Followers)• 2841 messages • Mar 13 2007 to Aug 18 2009
@darthvader (5,513 Following / 113,624 Followers)
• 484 messages• Jan 09 2007 to Sep 27 2009
Sample@stephendann• 274 Following / 355 Followers
– Supports Krishnamurthy et al (2008) 250 follower rule
• 2841 tweets – Start: Tue Mar 13 2007 11:53:01– End: Tue Aug 18 2009 07:29:30
• Data was captured from the timeline using the Sujathan (2009) “Twitter to pdf” software.
Categories and Results
Major Categories• Conversational
– Uses an @statement to address another user• Status
– An answer to “What are you doing now?”.• Pass along
– Tweets of endorsement of content• News
– Identifiable news content which is not UGC• Phatic
– Content independent connected presence• Spam
– Junk traffic, unsolicited automated posts, and other automated tweets generated without user consent
Results- @stephendann
Conversational (1473) 52%
news (13) 0%
Informal (103) 4%
status (974) 34%pass_along (278) 10%
phatic
Minor CategoriesConversational1. Query2. Referral3. Action4. Response
Status1. Personal2. Temporal3. Location4. Mechanical5. Physical6. Work7. Activity
Pass along1. RT2. UGC3. Endorsement
News1. Headlines2. Sport3. Event4. Weather
Phatic1. Greeting2. Fourth wall3. Broadcast4. Unclassifiable
Spam
Results - @stephendann
Conversational • Query
– Questions, question marks or polls • Referral
– An @response which contains URLs or recommendation of other Twitter users.
• Action– Activities involving other Twitter users
• Response– Catch-all classification for conversation @tweets
ConversationalCategory N % Exemplar
Action 77 3% *waves at @USERNAME*
Pass-along 66 2% @USERNAME Items under $1000 are exempt. http://is.gd/AV7K
Query 480 17% Invading Germany from France. Who's with me?
Response 850 30% @USERNAME Beware the polar bears.
Category N % Word Count
Words/ Sentence >6 letters Dictionary
Words Linguistic Inquiry Results
Action 77 3% 958 14.74 23.80 67.75 Conjunctions, Inhibition, Inclusive Biological processes
Pass-along 66 2% 1020 18.89 21.86 54.31 OtherP, Period
Query 480 17% 7032 10.13 21.22 75.33 Impersonal pronouns, Auxiliary verbs, Tentative, Discrepancy, QMark
Response 850 30% 13637 17.05 21.97 73.84 3rd pers plural
Conversational
action5% pass-along
4%
query33%
response58%
Status (1 of 2)• Personal
– Positive or negative sentiment in the form of personal opinion or emotional status
• Temporal– References to specific dates, times, statements
of temporal nature (waiting) and temporal action (“Time to” )
• Location– Geographic references and location statements,
including statements of traveling, location change
Status (2 of 2)• Mechanical
– Technology or mechanical systems • Physical
– Sensory experiences of a physical nature • Work
– Reference to work related activity • Activity
– Direct statements that answer “What are you doing now?”
Status
activity 35 1% Playing with the internet in the name of science
broadcast 140 5% Diplomacy is the art of saying "Nice doggy" until you find a big enough rock. Captaincy is the timely provision of large enough rocks.
location 69 2% Standing in a lecture theatre talking about Marketing Management.
mechanical 106 4% Well... I'm in trouble. Used 3829.060MB (62.322%) of your 6GB. You have 22 days remaining
personal 221 8% I liked Modest Mouse after they became famous.
physical 37 1% It's freezing out there this morning
temporal 170 6% Waiting for my 2pm performance review to start.
work 196 7% Firing off e-mail after e-mail to clear my to do list (knowing that's a great way to regenerate to do list items doesn't stop me or help me)
Category N % Word Count
Words/ Sentence
>6 letters
Dictionary Words Linguistic Inquiry Results
activity 35 1% 533 14.41 23.26 76.17 see, Ingestion,Achievement
broadcast 140 5% 2119 11.21 22.84 71.21 Friends, Quote
location 69 2% 1115 12.67 22.96 78.12 Articles, space
mechanical 106 4% 1985 13.88 22.02 70.43 Sadness
personal 221 8% 4121 19.35 19.05 80.71 Total function words, Common verbs, Past / Present tense, Adverbs, Cognitive processes,
physical 37 1% 658 16.87 23.10 81.91 Perceptual processes, feel, body, health
temporal 170 6% 3160 14.11 19.84 79.05 Prepositions
work 196 7% 3881 16.59 23.96 80.19 Work
Status
activty4% broadcast
14%
location7%
mechanical11%
personal23%
physical4%
temporal17%
work20%
Pass along• RT
– Any statement reproducing another Twitter status using the via @ or RT protocol
• UGC– Links to content created by the user
• Endorsement– Links to web content not created by the sender
Pass AlongCategory N % Exemplar
Pass along
endorsement 108 4% I'm looking myself up on Publish or Perish (http://rurl.org/iw4) to find a reference to a paper that cited me because I want to cite them
RT 48 2% L4D Survivors in Rockband2 singing L7 Pretend We're Dead. http://is.gd/BsVE (HT to @LesbianGamers ). It's seriously amazing.
Ugc 122 4% http://twitpic.com/2o1c1 - Bus Slogan Generator Time - http://is.gd/hU2Q
Category N % Word Count
Words/ Sentence >6 letters Dictionary
Words Linguistic Inquiry Results
endorsement 108 4% 1777 30.12 21.05 55.71 Parenth, Dash
RT 48 2% 893 16.85 24.75 54.31 SemiC
Ugc 122 4% 1679 39.98 20.61 52.89 Numbers, leisure
Pass Along
endorsement39%
RT17%
ugc44%
News• Headlines
– Coverage of breaking news and personal eye-witness accounts of news events
• Sport– Identifiable results of sporting events
• Event– Any tweet which represents the live discussion
of an identified or identifiable event• Weather
– Report of weather conditions without commentary
NewsCategory N % Exemplar
News
Event 13 0% Between NASA's satellite and autoanalysis of imagery, and Google Map data, scientific proof where there's smoke, there's fires #bcc2
Sport - - -
Headlines - - -
Category N % Word Count
Words/ Sentence >6 letters Dictionary
Words Linguistic Inquiry Results
News
Event 13 0% 192 10.67 22.92 65.62 Negative emotion, Anger Certainty Perceptual processes
Phatic• Greeting
– Statements of greetings to the broader Twitter community
• Fourth wall– Textual equivalent of comments made directly
to camera in television or cinema• Broadcast
– Textual soliloquy, monologue and undirected statements of opinion
• Unclassifiable– Unclassifiable strings of text
PhaticCategory N % Exemplar
Phatic
Action 30 1% *wanders through his twitter follower list, blocking all of the automated/spam follower accounts*
Fourth wall 49 2% Note to self: Just because you're carrying tiny vials of hypercaffeine is no reason to start calculating remote delivery systems for them.
Greeting 17 1% Good morning Twitterverse. How's the world outside?
Unclassifiable 7 0% AAAAAAAAAAAAAAARGH
Category N % Word Count
Words/ Sentence >6 letters Dictionary
Words Linguistic Inquiry Results
Action 30 1% 456 22.80 26.75 75.22 Anxiety, hear, motion
Fourthwall 49 2% 836 12.29 20.69 76.44 Negations, Quantifiers, Social processes, Humans, Affective processes, Causation, Exclusive
Greeting 17 1% 208 7.70 29.33 78.37 Future tense, Positive emotion Relativity, time
Unclassifiable 7 0% 6 3.00 33.33 33.33 NIL
Phatic
action
fourthwall
phatic
unclassifiable
FindingsNon commercial Twitter classification
Replicable across multiple accounts
Heavy duty liftingManual codingQualitative research
ImplicationsTwitter• Consumption analysis• Consumer framework • Not always a business framework
Future research
The Public Timeline versus the Classification Scheme
Questions
stephen@stephendann.net Or
@stephendann
ReferencesBöhme, G (2006) Technical Gadgetry: Technological Development in the Aesthetic Economy, Thesis Eleven, 86 (1): 54-66
Cetina, K K 2009, What is a Pipe? bama and the Sociological Imagination, Theory, Culture & Society 2009 26(5): 129–140
Crawford, K (2009)'Following you: Disciplines of listening in social media',Continuum,23:4,525 — 535
Dudley, E 2009, Editorial: Lines of Communication, Journal of Librarianship and Information Science 2009; 41; 131-134
Ettama, J 2009 New media and new mechanisms of public accountability, Journalism 2009; 10; 319-321
Fahmi, W S 2009, Bloggers' street movement and the right to the city. (Re)claiming Cairo's real and virtual "spaces of freedom", Environment and Urbanization 2009; 21; 89-107
Galer-Unti, R 2009, Guerilla Advocacy: Using Aggressive Marketing Techniques for Health Policy Change, Health Promotion Practice, 10; 325-327
Gay, P Plait, P, Raddick, J, Cain, F and Lakdawalla, E (2009) "Live Casting: Bringing Astronomy to the Masses in Real Time", CAP Journal, June 26-29
Henneburg, S. Scammell, M and O'Shaughnessy, N (2009) Political marketing management and theories of democracy, Marketing Theory 2009; 9; 165-188
Honeycutt, C and Herring, S C (2009) Beyond Microblogging: Conversation and Collaboration via Twitter, (2009). Proceedings of the Forty-Second Hawai’i International Conference on System Sciences (HICSS-42). Los Alamitos, CA: IEEE Press. 1-10, http://ella.slis.indiana.edu/~herring/honeycutt.herring.2009.pdf
Jansen, B, Zhang, M, Sobel, K and Chowdury, A (2009) Twitter power: Tweets as electronic word of mouth, Journal of the American Society for Information Science and Technology, 60(11):2169–2188, 2009 http://ist.psu.edu/faculty_pages/jjansen/academic/jansen_twitter_electronic_word_of_mouth.pdf
Java, A, Song, X, Finin, T and Tseng, B (2007) Why We Twitter: Understanding Microblogging Usage and Communities, Joint 9th WEBKDD and 1st SNA-KDD Workshop ’07 , August 12, 2007, p 56-65
ReferencesKrishnamurthy, B, Gill, P and Arlitt, M (2008) A Few Chirps About Twitter, WOSN'08, August
18, 2008, 19-24
Lariscy, R Avery, E J, Sweetser, K and Howes, P 2009 An examination of the role of online social media in journalists’ source mix, Public Relations Review 35 (2009) 314–316
Macintosh, A 2009, The emergence of digital governance, Significance, December, 176-178
Naaman, M, Boase, J and Lai, C-H (2010) Is it Really About Me? Message Content in Social Awareness Streams, CSCW 2010, February 6–10
Parslow, G, 2009, Commentary: Twitter for Educational Networking, BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION Vol. 37, No. 4, pp. 255–256, 2009
Pear Analytics (2009) Twitter Study – August 2009, http://www.pearanalytics.com/wp-content/uploads/2009/08/Twitter-Study-August-2009.pdf
Power, R and Forte, D 2008, War & Peace in Cyberspace: Don’t twitter away your organisation’s secrets, Computer Fraud and Security, August, 18-20
Zhao, D and Rosson, M B, How and Why People Twitter: The Role that Micro-blogging Plays in Informal Communication at Work, GROUP’04, May 10–13, 2009, 243-252
This work is licensed under the Creative Commons Attribution-Share Alike 2.5 Australia License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/2.5/au/
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