Examining User Generated Content on “Data Science” through Twitter

8/10/2019 Examining User Generated Content on Data Science through Twitter

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1Ana Crisostomostudent n. 10397124

Examining User Generated Content on Data Science through

Twitter

Ana Crisostomo

Student n. 10397124

Digital Methods

Assignment # 5

Supervisors: Bernhard Rieder / Erik Borra

07.12.2012

[email protected]


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Examining User Generated Content on Data Science through

Twitter

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.html


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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/1441/1852


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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.php


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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.php


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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/faq


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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/


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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-tweets


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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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Literature

Offline references

Boyd, Danah. Crawford, Kate. Critical Questions for Big Data: Provocations for a Cultural,

Technological, and Scholarly Phenomenon.Information, Communication, & Society

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Poell, Thomas. Borra, Erik. "Twitter, YouTube, and Flickr as Platforms of Alternative

Journalism: The Social Media Account of the 2010 Toronto G20 Protests."Journalism

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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Anand, Kunal. Tweetures. 2011. 2 December 2012.

.

Beevolve. An Exhaustive Study of Twitter Users Across the World. 10 October 2012.

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.

Bruns, Axel. Lang, Yuxian Eugene. Tools and Methods for Capturing Twitter Data during

Natural Disasters.First Monday 17. 4 (2 April 2012): 670-685. 4 December 2012.

.
http://whatspop.com/entry/tweetureshttp://whatspop.com/entry/tweetureshttp://www.beevolve.com/twitter-statistics/#e1http://www.beevolve.com/twitter-statistics/#e1http://www.firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3937/3193http://www.firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3937/3193http://www.firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3937/3193http://www.firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3937/3193http://www.firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3937/3193http://www.beevolve.com/twitter-statistics/#e1http://whatspop.com/entry/tweetures


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.

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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/


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Network Visualization 2 - Hashtag network for #dataviz



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Network Visualization 3 - Hashtag network for #datavis



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Network Visualization 4 - Hashtag network for #bigdata



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Appendix 2

0102030405060708090

100

Chart 5 - Klout score of top 15 most mentioned users

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

Chart 6 - Total number of tweets of top 15 most mentioned users


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0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

Chart 7 - Total number of followers of top 15 most mentioned users

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

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/


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Diagram 2Word Cluster for @bigdatablogs



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Diagram 3Word Cluster for @forbes



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Diagram 4Word Cluster for @harvardbiz



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Diagram 5Word Cluster for @ibmbigdata



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Diagram 6Word Cluster for @albertocairo



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Diagram 7Word Cluster for @benkerschberg



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Diagram 8Word Cluster for @bigdataexpo



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Diagram 9Word Cluster for @informaticacorp



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Diagram 10Word Cluster for @wsj



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Diagram 11Word Cluster for @ventanaresearch



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Diagram 12Word Cluster for @worldbank



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Diagram 13Word Cluster for @faceofbigdata



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Diagram 14Word Cluster for @sqlserver



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Diagram 15Word Cluster for @iabuk



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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

Documents

Examining User Generated Content on “Data Science” through Twitter