Abstract

Are users who comment on a variety of matters more likely to achieve high influence than those who delve into one fo-cused field? Do general Twitter hashtags, such as #lol, tend to be more popular than novel ones, such as #instant-lyinlove? Questions like these demand a way to detect top-ics hidden behind messages associated with an individual or a hashtag, and a gauge of similarity among these top-ics. Here we develop such an approach to identify clusters of similar hashtags by detecting communities in the hash-tag co-occurrence network. Then the topical diversity of a user’s interests is quantified by the entropy of her hashtags across different topic clusters. A similar measure is applied to hashtags, based on co-occurring tags. We find that high topical diversity of early adopters or co-occurring tags im-plies high future popularity of hashtags. In contrast, low diversity helps an individual accumulate social influence. In short, diverse messages and focused messengers are more likely to gain impact. Categories and Subject Descriptors [Information systems]: Information systems applications— Collaborative and social computing systems and tools, Social networking sites; [Information systems]: World Wide Web—Web applications, Web mining; [Human-centered