#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)


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.


Community Detection Online Social Network Community Detection Algorithm Topical Group Mutual Friend 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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