Towards Understanding Community Interests with Topic Modeling

Community plays an important role in shaping a network. Quantitatively interpreting a community is necessary for graph generalization which is used for privacy preserving, summarization, and dimensionality reduction in social network mining. However, there are few works in community detection focusing on making sense of the identified communities. In this paper, we study communities in the social media context and investigate structure-based communities from the perspective of community topical homophily. We train Latent Dirichlet Allocation topic model to capture the topics in the aggregated tweets of each user in a community and propose new distance metrics to quantify the topic similarity of individual users, cliques, and communities. By building a Twitter topic modeling system to interpret the communities identified by two community detection algorithms in a large scale Twitter topology, we discover evidence that Twitter users in a community show common interests in general. The major contributions of this paper lie in that it verifies the topical homophily in structure-based social media communities and proposes new metrics to quantitatively label the degree of the homophily and describe the theme of the communities.