Predicting User Participation in Social Networking Sites

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, Douban.com. The experimental results show the effectiveness of our approach.