LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity

Abstract

Predicting the popularity dynamics of Twitter hashtags has a broad spectrum of applications. Existing works have mainly focused on modeling the popularity of individual tweets rather than the popularity of the underlying hashtags. Hence, they do not consider several realistic factors for hashtag popularity. In this paper, we propose Large Margin Point Process (LMPP), a probabilistic framework that integrates hashtag-tweet influence and hashtag-hashtag competitions, the two factors which play important roles in hashtag propagation. Furthermore, while considering the hashtag competitions, LMPP looks into the variations of popularity rankings of the competing hashtags across time. Extensive experiments on seven real datasets demonstrate that LMPP outperforms existing popularity prediction approaches by a significant margin. Going further, LMPP can accurately predict the relative rankings of competing hashtags, offering additional advantage over the state-of-the-art baselines.