User clustering in a dynamic social network topic model for short text streams

Recently user clustering has become an increasingly important subject because of the high popularity of social medias like Twitter, Weibo and Facebook. The state-of-the-art algorithms of user clustering are all focused on the long or short text streams without considering factors of social network information, making them either unable to capture the social connectivity from short text streams or unable to conform to the sparsity, high-dimensionality and dynamically changing topics of short text streams. To address these issues, we propose a user clustering method named dynamic social network topic model (DSM) in this paper to cluster users by modeling their topics with dynamic features and social connectivity in short text streams. Experimental results show our topic model outperforms the state-of-the-art methods in the context of short text streams with social network information.