In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on atopic thatis ofinterest toher, andfollow theircollective tweets. Our key insight is that the List meta-data (names anddescriptions)providesvaluablesemantic cuesaboutwho the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, we can infer a user’s expertise by analyzing the meta-data of crowdsourced Lists that contain the user. We show that our methodology can accurately and comprehensively infer attributes of millions of Twitter users, including a vast majority ofTwitter’s influentialusers(basedonrankingmetrics like number of followers). Our work provides a foundation for building better search and recommendation services on Twitter. to the success of Twitter, but it also poses a big challenge: how can microbloggers tell who is who in Twitter? Knowing the credentials of a Twitter user can crucially help others determine how much trust or importance they should place in the content posted by the user. In this paper, we present the design and evaluation of a novel who-is-who inference system for users on the popular Twitter microblogging site. Figure 1 shows an illustrative tag cloud of attributes inferred by our service for Lada Adamic, who is an active Twitter user and a wellknown researcher in the area of social networks . Note that these attributes not only contain her biographical information (she is a professor at umsi, umich – University of Michigan’s School of Information), but also capture her expertise (she is an expert on social media, network-analysis, social networks, csresearch, hci, statphysics) as well as popular perceptions about her (she is a bigname, a thinker, and a goodblogger(s).
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