A Non-parametric Hidden Markov Clustering Model with Applications to Time Varying User Activity Analysis

Activity data of individual users on social media are easily accessible in this big data era. However, proper modeling strategies for user profiles have not been well developed in the literature. Existing methods or models usually have two limitations. The first limitation is that most methods target the population rather than individual users, and the second is that they cannot model non-stationary time-varying patterns. Different users in general demonstrate different activity modes on social media. Therefore, one population model may fail to characterize activities of individual users. Furthermore, online social media are dynamic and ever evolving, so are users' activities. Dynamic models are needed to properly model users' activities. In this paper, we introduce a non-parametric hidden Markov model to characterize the time-varying activities of social media users. An EM algorithm has been developed to estimate the parameters of the proposed model. In addition, based on the proposed model, we develop a clustering method to group users with similar activity patterns.