A dynamic nonparametric model for characterizing the topical communities in social streams

Latent variable models have proven to be a useful tool for discovering latent structures from observational data. However, the data in social networks often come as streams, i.e., both text content (e.g., emails, user postings) and network structure (e.g., user friendship) evolve over time. To capture the time-evolving latent structures in such social streams, we propose a fully nonparametric Dynamic Topical Community Model (nDTCM), where infinite latent community variables coupled with infinite latent topic variables in each epoch, and the temporal dependencies between variables across epochs are modeled via the rich-gets-richer scheme. We focus on characterizing three dynamic aspects in social streams: the number of communities or topics changes (e.g., new communities or topics are born and old ones die out); the popularity of communities or topics evolves; the semantics such as community topic distribution, community participant distribution and topic word distribution drift. Furthermore, we develop an effective online posterior inference algorithm for nDTCM, which is concordant with the online nature of social streams. Experiments using real-world data show the effectiveness of our model at discovering the dynamic topical communities in social streams.