It is not just what we say, but how we say them: LDA-based behavior-topic model

Textual information exchanged among users on online social network platforms provides deep understanding into user-s ’ interest and behavioral patterns. However, unlike tradi-tional text-dominant settings such as offline publishing, one distinct feature for online social network is users ’ rich inter-actions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic mod-el (B-LDA) which jointly models user topic interests and be-havioral patterns. We focus the study of the model on online social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich. We conduct experiments on real Twitter data to demonstrate that the topics obtained by our model are both informative and insightful. As an application of our B-LDA model, we also propose a Twitter followee rec-ommendation algorithm combining B-LDA and LDA, which we show in a quantitative experiment outperforms LDA with a significant margin.