Title: Topical homophily in online social systems,
Abstract: Understanding the dynamics of social interactions is crucial to comprehend
human behavior. The emergence of online social media has enabled access to data
regarding people relationships at a large scale. Twitter, specifically, is an
information oriented network, with users sharing and consuming information. In
this work, we study whether users tend to be in contact with people interested
in similar topics, i.e., topical homophily. To do so, we propose an approach
based on the use of hashtags to extract information topics from Twitter
messages and model users' interests. Our results show that, on average, users
are connected with other users similar to them and stronger relationships are
due to a higher topical similarity. Furthermore, we show that topical homophily
provides interesting information that can eventually allow inferring users'
connectivity. Our work, besides providing a way to assess the topical
similarity of users, quantifies topical homophily among individuals,
contributing to a better understanding of how complex social systems are
structured.