Community Topic Usage in Online Social Media

Abstract

Humans have a natural tendency to form social groups, and individual behaviours are thought to be strongly influenced by a salient sense of belonging to one or more such groups. It can be expected, therefore, that there will be behaviours that are specific to the group(s) to which a person currently feels they are interacting with and that some of these behaviours will manifest in topics and patterns of linguistic style associated with those groups. Here we explore this idea by attempting to identify group specific patterns of language usage in social media data from Twitter and Reddit. Topic models are used to infer patterns of language usage and group structures are either provided with the data (Reddit) inferred from the follower network (Twitter). We apply a Bayesian graphical model to infer community-topic associations, finding that substantially more coherent associations can often be identified than with a naive probability-based approach. Strong associations are found between groups and topics with both approaches, indicating that the methods used to (independently) identify groups and topics represent real underlying patterns of social communication and promising fruitful investigation of human social behaviour using these or similar techniques.