Top-N Hashtag Prediction via Coupling Social Influence and Homophily

Abstract

Considering the wide acceptance of the social media social influence starts to play very important role. Homophily has been widely accepted as the confounding factor for social influence. While literature attempts to identify and gauge the magnitude of the effects of social influence and homophily separately limited attention was given to use both sources for social behavior computing and prediction. In this work we address this shortcoming and propose neighborhood based collaborative filtering (CF) methods via the behavior interior dimensions extracted from the domain knowledge to model the data interdependence along time factor. Extensive experiments on the Twitter data demonstrate that the behavior interior based CF methods produce better prediction results than the state-of-the-art approaches. Furthermore, considering the impact of topic communication modalities (topic dialogicity, discussion intensiveness, discussion extensibility) on interior dimensions will lead to an improvement of 3%. Finally, the joint consideration of social influence and homophily leads to as high as 80.8% performance improvement in terms of accuracy when compared to the existing approaches.