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Examining User Generated Content on Data Science through
Ana Crisostomo
Student n. 10397124
Digital Methods
Assignment # 5
Supervisors: Bernhard Rieder / Erik Borra
07.12.2012
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Examining User Generated Content on Data Science through
Introduction
Founded in 2006 by Jack Dorsey, Twitter is currently one of the main players in the social
media sphere at a global level. Even though the company does not publish official reports on
the platform statistics on a regular basis[1]
, earlier on this year it announced having more
than 140 million active users and managing 340 million Tweets per day[2]
.
Categorized under the microblogging service category due to the 140 characters limitation
of its messages (tweets), it is considered by some as electronic word of mouth [3]providing
the content of the messages with a very specific character in terms of structure and
language (characterized, for example, by the usage of hashtags represented by the symbol
# as metadata tags for content association purposes), and temporality (focus on the
immediate present) which attracts interest from the business and corporate world, the
academic arena and even the artistic field (albeit for distinct purposes).
The business perspective might focus on user demographics, patterns and frequency of
usage, categorization of content and other relevant preferences in order to gather data
which, in most cases, is converted into marketing information[4]
. Academia might direct its
efforts to contextualize and explain the usage of the service, for instance, in event-based
situations of social and political relevance such as extensive natural catastrophes[5]
,
[1]The most complete set of official numbers on the platform usage in the companys blog dates from 2011:
http://blog.twitter.com/2011/03/numbers.html.
[2] According to numbers divulged on March 2012 on occasion of Twitters 6
th birthday:
http://blog.twitter.com/2012/03/twitter-turns-six.html.
[3] See Twitter power: Tweets as electronic word of mouth by Jansen et al:
http://onlinelibrary.wiley.com/doi/10.1002/asi.21149/full .
[4] On this topic, consult a recent study of Twitter users by Beevolve, a social media marketing company:
http://www.beevolve.com/twitter-statistics/.
[5] See Earthquake shakes Twitter users: real-time event detection by social sensors by Sakaki et al:
http://dl.acm.org/citation.cfm?id=1772777and "Tools and Methods for Capturing Twitter Data during Natural
Disasters" by Bruns and Lang.
http://blog.twitter.com/2011/03/numbers.htmlhttp://blog.twitter.com/2011/03/numbers.htmlhttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://onlinelibrary.wiley.com/doi/10.1002/asi.21149/fullhttp://onlinelibrary.wiley.com/doi/10.1002/asi.21149/fullhttp://www.beevolve.com/twitter-statistics/http://www.beevolve.com/twitter-statistics/http://dl.acm.org/citation.cfm?id=1772777http://dl.acm.org/citation.cfm?id=1772777http://dl.acm.org/citation.cfm?id=1772777http://www.beevolve.com/twitter-statistics/http://onlinelibrary.wiley.com/doi/10.1002/asi.21149/fullhttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://blog.twitter.com/2011/03/numbers.html8/10/2019 Examining User Generated Content on Data Science through Twitter
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elections[6]
, official political events[7]
, and social uprisings[8]
. Finally, the art world can use
Twitter data to explore moods and feelings[9]
, to portray unconventional visualizations[10]
or simply to stage playfulness[11]
.
The present academic research will study one specific topic (considered to hold currentrelevance) uniquely through communication processed via Twitter for a limited period of
time in order to try to reveal significant connections between the content, the users (often
labeled as twitterati) and the platform usage.
Questions
As briefly introduced, Twitter data can be utilized for diverse purposes in several areas. This
specific study aims at: 1) exploring to which extent a social media microblogging service as
Twitter plays a significant role in the communication of certain ideas around one specific
topic, and 2) unveiling the user dynamics which develop around the same topic.
Since Twitter is a platform heavily focused on the present and privileging quick and short
messages, this type of study lends itself more adequately to events and topics of current
relevance and demanding a certain urgency on the communication act with the purpose, for
example, of announcing, promoting or denouncing (one is excluding from this perspective
all communication which could be labeled as conversations of private nature between a
limited number of userssince that falls outside the scope of the current study).
The topic selected for the investigation is data sciencewhich has increasingly gained more
media significance (but also beyond the media realm) with its association to themes such as
[6]See Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment by Tumasjan
et al:http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852 .
[7] See Twitter, YouTube, and Flickr as platforms of alternative journalism: The social media account of the
2010 Toronto G20 protests by Poell and Borra.
[8] See For the ppl of Iran - #iranelection RT by the Digital Methods Initiative:
https://movies.issuecrawler.net/for_the_ppl_of_iran.html.
[9] As examples check, respectively, the Emoto interactive installation: http://blog.emoto2012.org/ and the
Twistori application:http://twistori.com.
[10]Look up the Tweetures visualization case:http://whatspop.com/entry/tweetures.
[11]On this matter, read about the Tasty Tweets project:http://www.kfrantzis.com/Tasty-Tweets.
http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852https://movies.issuecrawler.net/for_the_ppl_of_iran.htmlhttps://movies.issuecrawler.net/for_the_ppl_of_iran.htmlhttp://blog.emoto2012.org/http://blog.emoto2012.org/http://blog.emoto2012.org/http://twistori.com/http://twistori.com/http://twistori.com/http://whatspop.com/entry/tweetureshttp://whatspop.com/entry/tweetureshttp://whatspop.com/entry/tweetureshttp://www.kfrantzis.com/Tasty-Tweetshttp://www.kfrantzis.com/Tasty-Tweetshttp://www.kfrantzis.com/Tasty-Tweetshttp://www.kfrantzis.com/Tasty-Tweetshttp://whatspop.com/entry/tweetureshttp://twistori.com/http://blog.emoto2012.org/https://movies.issuecrawler.net/for_the_ppl_of_iran.htmlhttp://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/18528/10/2019 Examining User Generated Content on Data Science through Twitter
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information visualization and big data (the latter certainly being one of 2012s business
buzzwords).
The research questions for this study can then be stated as follows: 1)What are the most
common themes associated to data science in Twitter? 2) Are there specific sources ofinformation or actors which play an important role within this topical area? 3) Are there
visible patterns regarding the nature of the content associated with this topic?
The answer to these questions will hopefully shed some light into the treatment of the
subject through this particular microblogging platform.
Method
The dataset which enabled this research was available through the Twitter Analytics tool
(https://tools.digitalmethods.net/coword/twitter/analysis/index.php) which started
capturing tweets on 23/11/2012 (15:54 GMT) including, at least, one of the following
hashtags: #datascience, #bigdata, #dataviz and #datavis.
In order to work with a stable but still significant dataset, a period of one full week (7 days)
was selected (from 27/11/2012 to 03/12/2012) which summed up 21.356 tweets.
Regarding the hashtags, using one of the several web services which allow the visualization
of associated hashtags (in this case http://hashtagify.me/) it is possible to note that
#datascience does indeed have a strong connection with #bigdata (see Network
Visualization 1 onAppendix 1). However, the two other topics (which basically refer to one
topic written in two different manners) do not seem to be regularly associated with that
hashtag according to this tool (and between both, dataviz emerges as more common than
datavis see Network Visualizations 2 and 3 on Appendix 1). The theme big data
appears to be the connector between the topics data science and data visualization(see
Network Visualization 2 onAppendix 1). According to statistics provided by the same tool,
the most popular hashtag from the set is #bigdata (index of 56.4see Network Visualization
4 on Appendix 1) and the least popular #datascience (index of 25.3 see Network
Visualization 1 onAppendix 1). It will be of interest to ascertain if the findings of this study
corroborate this particular topical correlation or not and potentially suggest other topics for
future research.
https://tools.digitalmethods.net/coword/twitter/analysis/index.phphttps://tools.digitalmethods.net/coword/twitter/analysis/index.phphttps://tools.digitalmethods.net/coword/twitter/analysis/index.phphttp://hashtagify.me/http://hashtagify.me/http://hashtagify.me/https://tools.digitalmethods.net/coword/twitter/analysis/index.php8/10/2019 Examining User Generated Content on Data Science through Twitter
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not tweeting about the topic during, what is considered to be, the standard leisure time (see
Graph 1data beyond this week also verifies this particular pattern).
Graph 1Temporal distribution of Tweets, Users and Locations (including, at least, one of the hashtags
#datascience, #bigdata, #dataviz, #datavis) from 27/11/2012 to 03/12/2012
Source:https://tools.digitalmethods.net/coword/twitter/analysis/index.php
In terms of the most common hashtags (provided by the Hashtag Frequencyreport), there
is one which clearly outnumbers all the others: #bigdata (see Chart 1all extracted data is
available throughAppendix 5).
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
Chart 1 - Hashtag Frequency (Top 15 hashtags)
https://tools.digitalmethods.net/coword/twitter/analysis/index.phphttps://tools.digitalmethods.net/coword/twitter/analysis/index.phphttps://tools.digitalmethods.net/coword/twitter/analysis/index.phphttps://tools.digitalmethods.net/coword/twitter/analysis/index.php8/10/2019 Examining User Generated Content on Data Science through Twitter
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Considering all the hashtags which are referred a minimum of 5 times in the dataset and
performing a comparative analysis on their representation, then only two of the hashtags
(#bigdata and #dataviz) represent more than 50% of all hashtags included in tweets (or,
saying it differently, one out of two tweets would include either #bigdata or #dataviz).
If one excludes the top 2 hashtags (which were also included in the initial set), then it is
possible to visualize that 3 other topics gain relevance in this thematic arena: cloud,
analyticsand cloud computing(see Chart 3).
17,753
1,867
18,062
Chart 2 - Weight of #bigdata and #dataviz hashtags compared to all
other hashtags (referred a minimum of 5 times in the dataset)
bigdata
dataviz
others
0
200
400
600
800
1,000
1,200
1,400
1,600
Chart 3 - Hashtag Frequency (Top 15 hashtags excluding #bigdata and #dataviz)
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Moving forward to analyze the potentially most influential actors in this topical arena, the
top 15 most mentioned users were collected (data provided by the User Mention
Frequency reportsee Chart 4).
Within this top 15, there were mainly corporate professional accounts (including media,
business and non-profit organizations). Besides those, it was possible to find 2 accounts
which were related to events (cloudexpo and bigdataexpo even if under different titles,
they refer to the same event), 2 accounts belonging to individual users (albertocairo and
benkerschberg) and one for a book (thefaceofbigdata).
To examine further the level of influence of these top users, the Klout score (see Chart 5 on
Appendix 2)was added as well as the total number of followers (see Chart 7 onAppendix 2)
and then the total number of tweets (see Chart 6 onAppendix 2)as an indicator of activity.
Taking into account that Klout considers 40 as being an average score[15]
, then it is possible
to state that almost all of the most mentioned users can be considered influential in the
social media universe within that particular scale. Two accounts score above 90 (which is
considered to be very high): Forbes and The World Bank. Complementing this metric with
the number of followers, then The Wall Street Journal is, by far, the account with more
followers, followed then by Forbes and Harvard Business. In terms of activity, The Wall
Street Journal leads the ranking once more, followed by CloudExpo and Forbes. From these
[15]According to Klout: The average Klout Score is 40. Your Score is determined over a large period of time, and
is not necessarily representative of your number of followers and friends. Also, the Score is a reflection of
influence, not activity(http://klout.com/#/corp/faq).
0
50
100
150
200
250
Chart 4 - Mention Frequency (Top 15 on Number of Mentions)
http://klout.com/#/corp/faqhttp://klout.com/#/corp/faqhttp://klout.com/#/corp/faq8/10/2019 Examining User Generated Content on Data Science through Twitter
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numbers, we can conclude that content from The Wall Street Journal and Forbes has the
potential to reach more users more effectively.
One interesting aspect to add to this analysis is the fact that the number of user mentions
does not hold a correlation with the number of tweets sent by the respective user within
the specified period of time (see Chart 9). For instance, the accounts for Forbes, Harvard
Business and The Wall Street Journal have hardly sent any tweets on the topic (or none at
all) during that week (according to the results provided by the TA tool) and yet they are
included in the most mentioned users which indicates that the content being distributed is,
most likely, provenient from another source (which is not Twitter), such as the official
website of that institution.
Within this most mentioned users list, it is also of interest to understand what is the
relative attention paid to this topic in comparison with all other topics those users tweet
about. Using the Tweet Topic Explorer (see the 15 diagrams onAppendix 3), it is possible to
verify that the most influential accountsThe Wall Street Journal, Forbes, Harvard Business,
The World Bankdo not give prominence to these topics in their tweets (in some cases, the
hashtags researched do not even appear in these diagrams) as they are rather generalist.
The hashtags only seem to gain relevance in accounts which are specialized in this subject
such as: bigdatablogs, bigdataexpo/cloudexpo, faceofbigdata, ibmbigdata, informaticacorp
and the two individual user accounts.
218
195
174
147 147
111 109 108
90 9084 80 76 75 73
38
129
1 0
46
27
8
28
2 0
37
2
42
14
31
0
50
100
150
200
Chart 9 - Mention frequency VS Number of tweets sent for top 15 most mentioned
users
Mention
Frequency
# Tweets 27/11 -
03/12
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Regarding specific topical patterns, the Gephi rendition of the Co-hashtag Analysis (see
Network Visualization 5 on Appendix 4) confirms that #bigdata holds a very central and
unifying position in this thematic while #datascience, #dataviz and #datavis occupy a more
peripheral space. Other predominant topics concern cloud computing (#cloud,
#cloudcomputing) and analytics. It is also possible to find some companies with a
representative position in this network such as IBM, LinkedIn and Microsoft.
The type of content shared on this theme appears to be mostly informational since more
than 80% of the tweets include a link (see Chart 10). This value is significantly above the
average communicated in September 2010 by Twitter: just one year ago only 25% of the
tweets contained a link[16]
.
Chart 10 - % tweets containing links
Finally, one ending note regarding content analysis. It could be of interest to classify a
sample of tweets according to their content but, considering that the vast majority of
messages has essentially an informational nature (sharing an article on the topic, promoting
a training event or other educative material), it is very challenging to establish significant
categories within this area. The study could benefit from a report on URLs shared in order to
further investigate the influential actors and relevant content regarding this theme but thefunctionality providing these reports was not completed in time for the current research.
Discussion
Twitter as a microblogging platform enables what some authors label as electronic word of
[16]According to data from this report:http://techcrunch.com/2010/09/14/twitter-event/.
http://techcrunch.com/2010/09/14/twitter-event/http://techcrunch.com/2010/09/14/twitter-event/http://techcrunch.com/2010/09/14/twitter-event/http://techcrunch.com/2010/09/14/twitter-event/8/10/2019 Examining User Generated Content on Data Science through Twitter
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mouth: the content of the tweets is, by default, in the public domain (unless the user
explicitly sets his tweets to protected[17]
) and can theoretically reach a significant number
of people at least, all the web-savvy individuals who are pro-actively interested in the
subject matter.
Some initial studies stated that most of the content shared via this service held minimal
pass-along value[18]
but this view has suffered modifications in the last two years with the
rise of the platform as a crucial agent in critical situations of political and social nature[19]
and unpredictable natural catastrophes[20]
. This association has propelled several ambitious
projects catering specifically for those situations[21]
.
In such cases, the source of the content is originally an individual user and the content of
the message is directly related to the experienced individual context of the same. However,
how do the content and the service usage change outside these extreme situations?
The topic of this research held current relevance (especially in the corporate and academic
fields) but it was not of urgent nature. In this scenario, the most influential agents are
institutions and not individual users. This fact does not imply that individual users do not
play an important part in the dissemination of information through the platform they
actually do but the content being shared is not originally produced by them but byrenowned institutions which have achieved a high level of reputation and credibility prior to
the existence of the microblogging platform itself. Individual users can still earn a position of
[17] As referred by Twitter: http://support.twitter.com/articles/14016-about-public-and-protected-tweets . On
this account it may be of interest to read an article on a legal decision related to issues of privacy and
ownership on tweet content:http://www.salon.com/2012/04/26/who_owns_your_tweets/.
[18] See the results from a study conducted by an internet marketing company (Pear) in 2009:
http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-
babble/andhttp://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/
[19] The Arab Spring and related events being the most cited examples in this case:
http://www.foreignpolicy.com/articles/2011/06/20/the_revolution_will_be_tweeted.
[20]One example is the 2011 9.0 scale earthquake in Japan where the service played a role in disseminating
news on specific locations. However, some authors also state that a more efficient usage of the tool can be
promoted in future situations:http://www.sciencedaily.com/releases/2011/04/110415154734.htm .
[21]On this matter see The Global Twitter Hearbeat project by SGI in partnership with the University of Illinois
https://www.facebook.com/sgiglobal/app_164226463720371.
http://support.twitter.com/articles/14016-about-public-and-protected-tweetshttp://support.twitter.com/articles/14016-about-public-and-protected-tweetshttp://support.twitter.com/articles/14016-about-public-and-protected-tweetshttp://www.salon.com/2012/04/26/who_owns_your_tweets/http://www.salon.com/2012/04/26/who_owns_your_tweets/http://www.salon.com/2012/04/26/who_owns_your_tweets/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.foreignpolicy.com/articles/2011/06/20/the_revolution_will_be_tweetedhttp://www.foreignpolicy.com/articles/2011/06/20/the_revolution_will_be_tweetedhttp://www.sciencedaily.com/releases/2011/04/110415154734.htmhttp://www.sciencedaily.com/releases/2011/04/110415154734.htmhttp://www.sciencedaily.com/releases/2011/04/110415154734.htmhttps://www.facebook.com/sgiglobal/app_164226463720371https://www.facebook.com/sgiglobal/app_164226463720371https://www.facebook.com/sgiglobal/app_164226463720371http://www.sciencedaily.com/releases/2011/04/110415154734.htmhttp://www.foreignpolicy.com/articles/2011/06/20/the_revolution_will_be_tweetedhttp://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.salon.com/2012/04/26/who_owns_your_tweets/http://support.twitter.com/articles/14016-about-public-and-protected-tweets8/10/2019 Examining User Generated Content on Data Science through Twitter
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relevance producing content which is considered to be worth sharing but the effort to
achieve such has to be proportionally higher (in comparison to that of the institutions).
Within this thematic area, the individual user performs an important activity in promoting
(while not necessarily producing) content but these actions tend to reflect and perpetuatepower relations which exist outside (and, to a certain extent one could claim, independently
of) the platform itself.
Additionally, some authors are rather skeptical of Twitter based research since a significant
number of these studies does not acknowledge the limitations of the data: Twitter does not
represent the global population, the number of accounts does not provide the number of
users (some users have multiple accounts and some accounts are used by multiple users)
and many users are just bots [22].
Considering the argument regarding the Web 2.0 industry that the more one uses and
contributes to these platforms, the more valuable they become[23]
, it could be of academic
interest to critically examine more closely the type of contributions being made related to
certain topics with special interest for the business and academic areas and try to ascertain
if the content being shared holds primarily a promotional or an educational character and
who are the main actors on an influential scale and how representative are theycontributions.
[22] On this matter read Critical Questions for Big Data: Provocations for a Cultural, Technological, and
Scholarly Phenomenonby Boyd and Crawford.
[23]
According to Shirky as cited by Rogers in Post-Demographic Machines - Studying Social Networking Sitesin Walled Garden(page 35).
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13. 6 (2012): 695-713.
Rogers, Richard. "Post-demographic Machines." Walled Garden. Eds. Annet Dekker and
Annette Wolfsberger. Amsterdam: Virtueel Platform, 2009. 29-39.
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.
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https://movies.issuecrawler.net/for_the_ppl_of_iran.htmlhttps://movies.issuecrawler.net/for_the_ppl_of_iran.htmlhttps://movies.issuecrawler.net/for_the_ppl_of_iran.htmlhttp://blog.emoto2012.org/http://blog.emoto2012.org/http://twistori.com/http://www.sciencedaily.com/releases/2011/04/110415154734.htmhttp://www.sciencedaily.com/releases/2011/04/110415154734.htmhttp://onlinelibrary.wiley.com/doi/10.1002/asi.21149/fullhttp://onlinelibrary.wiley.com/doi/10.1002/asi.21149/fullhttp://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://klout.com/#/corp/faqhttp://klout.com/#/corp/faqhttp://klout.com/#/corp/klout_scorehttp://klout.com/#/corp/klout_scorehttp://klout.com/#/corp/faqhttp://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-continuing-the-conversation/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://www.pearanalytics.com/blog/2009/twitter-study-reveals-interesting-results-40-percent-pointless-babble/http://onlinelibrary.wiley.com/doi/10.1002/asi.21149/fullhttp://www.sciencedaily.com/releases/2011/04/110415154734.htmhttp://twistori.com/http://blog.emoto2012.org/https://movies.issuecrawler.net/for_the_ppl_of_iran.html8/10/2019 Examining User Generated Content on Data Science through Twitter
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Siegler, MG. Twitter Hatches The New Twitter.com A New Two-Pane Experience (Live).
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http://www.salon.com/2012/04/26/who_owns_your_tweets/http://www.salon.com/2012/04/26/who_owns_your_tweets/http://dl.acm.org/citation.cfm?id=1772777http://dl.acm.org/citation.cfm?id=1772777https://www.facebook.com/sgiglobal/app_164226463720371https://www.facebook.com/sgiglobal/app_164226463720371http://techcrunch.com/2010/09/14/twitter-event/http://techcrunch.com/2010/09/14/twitter-event/http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852http://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852http://blog.twitter.com/2011/03/numbers.htmlhttp://blog.twitter.com/2011/03/numbers.htmlhttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://support.twitter.com/articles/14016-about-public-and-protected-tweetshttp://support.twitter.com/articles/14016-about-public-and-protected-tweetshttp://support.twitter.com/articles/14016-about-public-and-protected-tweetshttp://blog.twitter.com/2012/03/twitter-turns-six.htmlhttp://blog.twitter.com/2011/03/numbers.htmlhttp://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1441/1852http://techcrunch.com/2010/09/14/twitter-event/https://www.facebook.com/sgiglobal/app_164226463720371http://dl.acm.org/citation.cfm?id=1772777http://www.salon.com/2012/04/26/who_owns_your_tweets/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Zorina, Kat. van der Vleuten, Ruben. Frantzis, Kostantinos. Tasty Tweets. 2012. 2
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http://www.kfrantzis.com/Tasty-Tweetshttp://www.kfrantzis.com/Tasty-Tweetshttp://www.kfrantzis.com/Tasty-Tweets8/10/2019 Examining User Generated Content on Data Science through Twitter
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Appendix 1
Network Visualization 1 - Hashtag network for #datascience
Source:http://hashtagify.me
http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Network Visualization 2 - Hashtag network for #dataviz
Source:http://hashtagify.me
http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Network Visualization 3 - Hashtag network for #datavis
Source:http://hashtagify.me
http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Network Visualization 4 - Hashtag network for #bigdata
Source:http://hashtagify.me
http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/http://hashtagify.me/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Appendix 2
0102030405060708090
100
Chart 5 - Klout score of top 15 most mentioned users
0
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Chart 6 - Total number of tweets of top 15 most mentioned users
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0
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Chart 7 - Total number of followers of top 15 most mentioned users
0
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Chart 8 - Total number of following of top 15 most mentioned users
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Appendix 3
Diagram 1Word Cluster for @cloudexpo
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 2Word Cluster for @bigdatablogs
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 3Word Cluster for @forbes
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 4Word Cluster for @harvardbiz
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 5Word Cluster for @ibmbigdata
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 6Word Cluster for @albertocairo
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 7Word Cluster for @benkerschberg
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 8Word Cluster for @bigdataexpo
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 9Word Cluster for @informaticacorp
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 10Word Cluster for @wsj
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 11Word Cluster for @ventanaresearch
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 12Word Cluster for @worldbank
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 13Word Cluster for @faceofbigdata
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 14Word Cluster for @sqlserver
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Diagram 15Word Cluster for @iabuk
Source:http://tweettopicexplorer.neoformix.com/
http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/http://tweettopicexplorer.neoformix.com/8/10/2019 Examining User Generated Content on Data Science through Twitter
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Appendix 4
Network Visualization 5Co-hashtag Analysis as rendered by Gephi
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Appendix 5
Files with extracted data from the Twitter Analytics tool:
datascience__2012-11-27_2012-12-03__hashtag_min5(this file contains 4
worksheets)
datascience__2012-11-27_2012-12-03__mention_min5(this file contains 4
worksheets)
https://docs.google.com/spreadsheet/ccc?key=0AijGdqUTikIqdGM3Ny1WaUlXX2hYQXZPQWkzVG94Y2chttps://docs.google.com/spreadsheet/ccc?key=0AijGdqUTikIqdGM3Ny1WaUlXX2hYQXZPQWkzVG94Y2chttps://docs.google.com/spreadsheet/ccc?key=0AijGdqUTikIqdEl2QnhubVFZVUc3MnRlWjFabGZnYmchttps://docs.google.com/spreadsheet/ccc?key=0AijGdqUTikIqdEl2QnhubVFZVUc3MnRlWjFabGZnYmchttps://docs.google.com/spreadsheet/ccc?key=0AijGdqUTikIqdEl2QnhubVFZVUc3MnRlWjFabGZnYmchttps://docs.google.com/spreadsheet/ccc?key=0AijGdqUTikIqdGM3Ny1WaUlXX2hYQXZPQWkzVG94Y2c