Social-Topical Affiliations: The Interplay between Structure and Popularity

Information popularity and social relationships are intimately connected. However, measuring the extent to which they affect each other has remained an open question. Because we now have access to rich and large data sets from online social networks, we can begin to quantitatively understand the interplay between them. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks — who is friends with whom — and the set structure of topical affiliations — who talks about what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our study is Twitter, where we look at the social network of both follower relationships and communication relationships, alongside the affiliations outlined by the hashtags used by people to label their communications. On Twitter, we demonstrate how the hashtags that a user adopts can be used to predict their social relationships, and also how the social relationships between the adopters of a hashtag can be used to predict the future popularity of that hashtag. Importantly, we find that both relationships are driven by highly computationally simple structural determinants. While our analysis focuses on Twitter, we view our analysis of social-topical affiliations as broadly applicable to a host of diverse affiliations, including the movies people watch, the brands people like, or the locations people frequent.