On the Interplay between Social and Topical Structure

TL;DR

This paper investigates how social network structures and topical interests, exemplified by Twitter hashtags, influence each other and can be used to predict social relationships and hashtag popularity, revealing that simple structural features are highly effective.

Contribution

It demonstrates the mutual predictability between social ties and topical interests on Twitter, highlighting the predictive power of simple structural features for both social relationships and hashtag popularity.

Findings

Hashtag usage can predict social relationships.

Social ties among initial adopters forecast hashtag popularity.

Weak directed ties are more predictive of hashtag success than strong reciprocated ties.

Abstract

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.