#FewThingsAboutIdioms: Understanding Idioms and Its Users in the Twitter Online Social Network

  • Koustav Rudra
  • Abhijnan Chakraborty
  • Manav Sethi
  • Shreyasi Das
  • Niloy Ganguly
  • Saptarshi Ghosh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)

Abstract

To help users find popular topics of discussion, Twitter periodically publishes ‘trending topics’ (trends) which are the most discussed keywords (e.g., hashtags) at a certain point of time. Inspection of the trends over several months reveals that while most of the trends are related to events in the off-line world, such as popular television shows, sports events, or emerging technologies, a significant fraction are not related to any topic / event in the off-line world. Such trends are usually known as idioms, examples being #4WordsBeforeBreakup, #10thingsIHateAboutYou etc. We perform the first systematic measurement study on Twitter idioms. We find that tweets related to a particular idiom normally do not cluster around any particular topic or event. There are a set of users in Twitter who predominantly discuss idioms – common, not-so-popular, but active users who mostly use Twitter as a conversational platform – as opposed to other users who primarily discuss topical contents. The implication of these findings is that within a single online social network, activities of users may have very different semantics; thus, tasks like community detection and recommendation may not be accomplished perfectly using a single universal algorithm. Specifically, we run two (link-based and content-based) algorithms for community detection on the Twitter social network, and show that idiom oriented users get clustered better in one while topical users in the other. Finally, we build a novel service which shows trending idioms and recommends idiom users to follow.

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References

  1. 1.
    Berardi, G., Esuli, A., Marcheggiani, D., Sebastiani, F.: ISTI@TREC Microblog Track 2011: Exploring the Use of Hashtag Segmentation and Text Quality Ranking. In: NIST TREC (2011)Google Scholar
  2. 2.
    Bhattacharya, P., Ghosh, S., Kulshrestha, J., Mondal, M., Zafar, M.B., Ganguly, N., Gummadi, K.P.: Deep Twitter Diving: Exploring Topical Groups in Microblogs at Scale. In: ACM CSCW (2014)Google Scholar
  3. 3.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring User Influence in Twitter: The Million Follower Fallacy. In: Proc. AAAI ICWSM (May 2010)Google Scholar
  4. 4.
    Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: crowdsourcing search for topic experts in microblogs. In: Proc. ACM SIGIR (2012)Google Scholar
  5. 5.
    Grabowicz, P.A., Aiello, L.M., Eguiluz, V.M., Jaimes, A.: Distinguishing topical and social groups based on common identity and bond theory. In: Proc. ACM WSDM (2013)Google Scholar
  6. 6.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proc. World Wide Web Conference (WWW) (2010)Google Scholar
  7. 7.
    Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.: Twitter Trending Topic Classification. In: Proc. IEEE International Conference on Data Mining Workshops (2011)Google Scholar
  8. 8.
    McMillan, D., Chavis, D.: Sense of community: A definition and theory. Journal of Community Psychology 14(1), 6–23 (1986)CrossRefGoogle Scholar
  9. 9.
    Naaman, M., Becker, H., Gravano, L.: Hip and trendy: Characterizing emerging trends on Twitter. Journal of the American Society for Information Science and Technology 62(5), 902–918 (2011)CrossRefGoogle Scholar
  10. 10.
    Prentice, D.A., Miller, D.T., Lightdale, J.R.: Asymmetries in attachments to groups and to their members: Distinguishing between common-identity and common-bond groups. Personality and Social Psychology Bulletin 20(5), 484–493 (1994)CrossRefGoogle Scholar
  11. 11.
    Chakraborty, A., Ghosh, S., Ganguly, N.: Detecting overlapping communities in folksonomies. In: Proc. ACM Hypertext Conference (2012)Google Scholar
  12. 12.
    Ren, Y., Kraut, R., Kiesler, S.: Applying Common Identity and Bond Theory to Design of Online Communities. Organization Studies 28(3), 377–408 (2007)CrossRefGoogle Scholar
  13. 13.
    Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proc. World Wide Web Conference (WWW), pp. 695–704 (2011)Google Scholar
  14. 14.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105, 1118–1123 (2008)CrossRefGoogle Scholar
  15. 15.
    Sassenberg, K.: Common bond and common identity groups on the Internet: Attachment and normative behavior in on-topic and off-topic chats. Group Dynamics Theory Research And Practice 6(1), 27–37 (2002)CrossRefGoogle Scholar
  16. 16.
    Sharma, N., Ghosh, S., Benevenuto, F., Ganguly, N., Gummadi, K.: Inferring Who-is-Who in the Twitter Social Network. In: Proc. WOSN Workshop (2012)Google Scholar
  17. 17.
    Twitter Help Center — Twitter’s suggestions for who to follow. https://support.twitter.com/articles/227220-twitter-s-suggestions-for-who-to-follow
  18. 18.
    Wagner, C., Liao, V., Pirolli, P., Nelson, L., Strohmaier, M.: It’s not in their tweets: Modeling topical expertise of twitter users. In: Proc. ASE/IEEE SocialCom (2012)Google Scholar
  19. 19.
    Chakraborty, A., Ghosh, S.: Clustering hypergraphs for discovery of overlapping communities in folksonomies. In: Dynamics on and of Complex Networks, vol. 2. Springer (2013)Google Scholar
  20. 20.
    Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on Twitter. In: Proc. World Wide Web Conference (WWW) (2011)Google Scholar
  21. 21.
    Zubiaga, A., Spina, D., Fresno, V., Martínez, R.: Real-Time Classification of Twitter Trends. Journal of the American Society for Information Science and Technology (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Koustav Rudra
    • 1
  • Abhijnan Chakraborty
    • 1
  • Manav Sethi
    • 1
  • Shreyasi Das
    • 1
  • Niloy Ganguly
    • 1
  • Saptarshi Ghosh
    • 2
    • 3
  1. 1.Department of CSEIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Max Planck Institute for Software SystemsKaiserslauternGermany
  3. 3.Department of CSTIndian Institute of Engineering Science and Technology ShibpurHowrahIndia

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