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

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 sig-nificant 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.