  People's interests and people's social relationships are intuitively
connected, but understanding their interplay and whether they can help predict
each other has remained an open question. We examine the interface of two
decisive structures forming the backbone of online social media: the graph
structure of social networks - who connects with whom - and the set structure
of topical affiliations - who is interested in 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 analysis is Twitter, a
complex social network of both follower relationships and communication
relationships. On Twitter, "hashtags" are used to label conversation topics,
and we examine hashtag usage alongside these social structures.
  We find that the hashtags that users adopt can predict their social
relationships, and also that the social relationships between the initial
adopters of a hashtag can predict the future popularity of that hashtag. By
studying weighted social relationships, we observe that while strong
reciprocated ties are the easiest to predict from hashtag structure, they are
also much less useful than weak directed ties for predicting hashtag
popularity. Importantly, we show that computationally simple structural
determinants can provide remarkable performance in both tasks. While our
analyses focus on Twitter, we view our findings as broadly applicable to
topical affiliations and social relationships in a host of diverse contexts,
including the movies people watch, the brands people like, or the locations
people frequent.
