Predicting User Mention Behavior in Social Networks

Abstract

Mention is an important interactive behavior used to explicitly refer to target users for specific information in social networks. Understanding user mention behavior can provide important insights into questions of human social behavior and improve design of social network platforms. However, most previous works mainly focus on mentioning for the effect of information diffusion, few researches consider the problem of mention behavior prediction. In this paper, we propose an intuitive approach to predict user mention behavior using link prediction method. Specifically, we first formulate user mention prediction problem as a classification task, and then extract new features including semantic interest match, social tie, mention momentum and interaction strength to improve the performance of prediction. To evaluate the proposed approach, we conduct extensive experiments on Twitter dataset. The experimental results clearly show that our approach has 15% increase in precision compared with the best baseline method.