Inferring Who-Is-Who in the Twitter Social Network
In this paper, the authors design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Their methodology exploits the lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collective tweets. Their key insight is that the List meta-data (names and descriptions) provides valuable semantic cues about who the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, they can infer a user's expertise by analyzing the meta-data of crowd-sourced Lists that contain the user.