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Twitter in Academic Conferences

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Twitter in Academic Conferences: Usage, Networking and Participation over Time
ACM Conference on Hypertext and Social Media 2014
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http://dl.acm.org/citation.cfm?doid=2631775.2631826
http://dx.doi.org/10.1145/2631775.2631826
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Xidao Wen, University of Pittsburgh
Yu-Ru Lin, University of Pittsburgh
Christoph Trattner, Know-Center
Denis Parra, Pontificia Universidad Católica de Chile

Published in: Engineering
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Twitter in Academic Conferences

  1. 1. Twitter in Academic Conferences: Usage, Networking and Participation over Time Xidao Wen, University of Pittsburgh Yu-Ru Lin, University of Pittsburgh Christoph Trattner, Know-Center *Denis Parra, Pontificia Universidad Católica de Chile ACM International Conferencia on Hypertext and Social Media, Santiago, 2014 *Presenter
  2. 2. Outline • Context & Motivation of this research • Research Questions • Question 1: Communication Types Over Time • Question 2: Network Evolution over Time • Question 3: Predicting Re-visiting over Time • Conclusions and Future Work
  3. 3. Context • An approach to Social Computing Research that I heard in a talk by Bob Kraut to study online social communities* *(More Precisely, what I recall from that talk) Robert Kraut Carnegie Mellon University
  4. 4. Context • An approach to Social Computing Research that I heard in a talk by Bob Kraut to study online social communities Understand Communities (behavior) 1st Step
  5. 5. Context • An approach to Social Computing Research that I heard in a talk by Bob Kraut to study online social communities Understand Communities (behavior) 1st Step Modify/Enhance Communities Behavior 2nd Step
  6. 6. In this work, we study Twitter at Conferences • … and study what conference attendees are currently using, and how that usage has evolved over time: Twitter Understand Communities (behavior) • How groups of people interact with each other on Twitter during academic conferences, In Proceedings of the 2014 ACM Conference on Computer Supported Cooperative Work (CSCW 2014), ACM, Baltimore, Maryland, USA
  7. 7. Many studies doing similar small-sized analysis • C. Ross, M. Terras, C. Warwick, and A. Welsh, “Enabled backchannel: conference twitter use by digital humanists,” Journal of Documentation, vol. 67, no. 2, pp. 214–237, 2011. • W. Reinhardt, M. Ebner, G. Beham, and C. Costa, “How people are using twitter during conferences,” Creativity and innovation Competencies on the Web, p. 145, 2009. • M. Ebner, “Introducing live microblogging: How single presentations can be enhanced by the mass.,” Journal of research in innovative teaching, vol. 2, no. 1, 2009. • J. Letierce, A. Passant, J. G. Breslin, and S. Decker, “Using twitter during an academic conference: The# iswc2009 use-case.,” in ICWSM, 2010.
  8. 8. Many studies doing similar small-sized analysis • C. Ross, M. Terras, C. Warwick, conference twitter use 1. by All of and these A. Welsh, works “Enabled backchannel: digital humanists,” Journal of Documentation, vol. 67, no. 2, pp. 214–consider 237, 2011. only a few • W. Reinhardt, M. Ebner, G. Beham, and C. Costa, “How people are using twitter during conferences,” Creativity and innovation Competencies on the Web, p. 145, conferences 2009. in specific • M. Ebner, “Introducing live microblogging: domains. How single presentations can be enhanced by the mass.,” Journal of research in innovative teaching, vol. 2, no. 1, 2009. • J. Letierce, A. Passant, J. G. Breslin, and S. Decker, “Using twitter during an academic conference: The# iswc2009 use-case.,” in ICWSM, 2010. 2. None of them have studied them over time
  9. 9. This Paper: Twitter in Conferences over Time • 16 conferences over 5 years in CS & IS
  10. 10. This Paper: Twitter in Conferences over Time
  11. 11. This Paper: Twitter in Conferences over Time • RQ1: Do users use Twitter more for socializing with peers or for information sharing during conferences? How has such use of Twitter during conferences changed over the years? • RQ2: What are the structures of conversation and information sharing networks in individual conferences? Have these network structures changed over time? • RQ3: Do users participate on Twitter for the same conference over consecutive years? To what extent can we predict users’ future conference participation?
  12. 12. This Paper: Twitter in Conferences over Time Types of communication over time • RQ1: Do users use Twitter more for socializing with peers or for information sharing during conferences? How has such use of Twitter during conferences changed over the years? • RQ2: What are the structures of conversation and information sharing networks in individual conferences? Have these network structures changed over time? • RQ3: Do users participate on Twitter for the same conference over consecutive years? To what extent can we predict users’ future conference participation?
  13. 13. This Paper: Twitter in Conferences over Time • RQ1: Do users use Twitter more for socializing with peers or for information sharing during conferences? How has such use of Twitter during conferences changed over the years? • RQ2: What Network are the structures changes of conversation and information sharing networks in individual conferences? Have these over network time structures changed over time? • RQ3: Do users participate on Twitter for the same conference over consecutive years? To what extent can we predict users’ future conference participation?
  14. 14. This Paper: Twitter in Conferences over Time • RQ1: Do users use Twitter more for socializing with peers or for information sharing during conferences? How has such use of Twitter during conferences changed over the years? • RQ2: What are the structures of conversation and information sharing networks in individual conferences? Have these network structures changed over time? • RQ3: Do users participate on Twitter for the same conference over consecutive years? To what extent can we predict users’ future conference participation? Prediction of User Participation
  15. 15. RQ1: Types of Conversation over Time Conference Tweets (line) vs. Random Sample (dashed)
  16. 16. RQ1: Types of Conversation over Time Conference Tweets: - RT and URLs increase over time
  17. 17. RQ2: Network Changes over Time Conversation Network: Mentions & Replies vs Retweet Network
  18. 18. RQ2: Network Changes over Time
  19. 19. RQ2: Network Changes over Time Weakly Connected Components: Conversation network: get more scattered. RT network stays the same over time.
  20. 20. RQ3: Prediction of User Participation • Can we tell whether someone will return the coming year to the conference based on her participation? AUC Results Baseline: Number of tweets in the User Timeline (with + without hashtag during the conference weeks) Your activity over the conference week is a good baseline predictor
  21. 21. RQ3: Prediction of User Participation • Can we tell whether someone will return the coming year to the conference based on her participation? AUC Results Baseline: Number of tweets in the User Timeline (with + without hashtag during the conference weeks) Only #hashtagged tweets doesn’t help much
  22. 22. RQ3: Prediction of User Participation • Can we tell whether someone will return the coming year to the conference based on her participation? AUC Results Baseline: Number of tweets in the User Timeline (with + without hashtag during the conference weeks) If you get retweeted by many people, or by important people
  23. 23. Conclusions & Future Work • Usage of Twitter in Conferences has increase over time and has changed considerable: people are more likely to RT and share URLs than to “talk” (replies and mentions) by Twitter • Over time, the conversations get more scattered, but not the “advertisement” or “resource sharing” of the RT network • Total Twitter activity over the conference week helps to predict returning to the conference, not only #hashtagged Twitter activity. • CURRENT EXTENSION: use content-based features, sentiment analysis, consider hypotheses of “communities of practice”
  24. 24. Limitations • The analyses only considered types of messages (re-tweets, mentions, URLs) and social network analysis’ metrics. • Obtaining a representative sample of Twitter is a problem not solve (at least for researchers in academia that can use only the Twitter API, or other 3rd party APIs such as Topsy) • We are covering a sample of conferences in the domain of Computer and Information Science.
  25. 25. A little spoiler… Subjectivity: Does this message have an opinion is rather a fact?  “I think this is a nice talk” (subjective ++)  There are 10 people in this room ( subjective-- )
  26. 26. A little spoiler… #ht2014? #ht2014 ? Polarity HT
  27. 27. Thanks! • And don’t forget today the social event (is included with the conference registration) at • Hotel Plaza San Francisco, 8:30pm
  28. 28. … but then, I decided to give a step back • Our first attempt was studying 4 conference-communities (Wen, Parra, Trattner at CSCW 2014) and see how groups of conference attendees communicate on Twitter • Faculty / Senior research students / Junior research students • Others • We expected to have an important participation from newcomers to the conferences.
  29. 29. How this Research Started • It started by creating a tool, Conference Navigator, in order to support conference attendees Modify/Enhance Communities Behavior • Conference Navigator provides a ranking of top bookmarked talks, recommendation of talks, “People who bookmarked this, also bookmarked …”
  30. 30. … but then, I decided to give a step back • Our first attempt was studying 4 conference-communities (Wen, Parra, Trattner at CSCW 2014) and see how groups of conference attendees communicate on Twitter • Faculty / Senior research students / Junior research students • Others • We expected to have an important participation from newcomers during the conferences. We found out that Twitter might not be always the best entrance for newcomers in a research community.
  31. 31. Random Sampling • Use of TOPSY API: Search for a –or- b –or- …. –z- • Pick 2/3 of the hours in a year (24 x 365) • Per each hour sampled, pick randomly 2 minutes • As a result, there will be a lot of tweets in several pages. Pick randomly a result page • Total tweets sampled: ~ 5.8 million tweets (2009 -2013) • Distribution of RT, replies, URLs is not significantly different than D. Boyd, S. Golder, and G. Lotan, “Tweet, tweet, retweet: Conversational aspects of retweeting on twitter,” in System Sciences (HICSS), 2010 43rd Hawaii InternationalConference on, pp. 1–10, IEEE, 2010.

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