Predicting User Retweeting Behavior in Social Networks With a Novel Ensemble Learning Approach

Information sharing through online social networks has become a main mechanism by which people share information with their friends through retweeting behaviors, which may result in a variety of information diffusion cascades on social media such as Facebook, Twitter, and Weibo. Predicting user retweeting behavior in those social networks is extremely challenging. To complete the prediction task, identifying factors that affect retweeting behavior and constructing efficient model are necessary. In this paper, we study heterogeneous relation networks by considering various social interactions, which reflect how a particular retweeting action is affected by the social behaviors performed by the sender and the receiver of the retweet. We then generate various features from our identified factors belonging to three dimensions - content semantics, user diffusion behavior, and network structure. Moreover, we cast our prediction problem as an ensemble learning problem and propose a novel ensemble learning approach to solve the problem. Combing the generated features and the novel ensemble learning approach, we then propose a model named Retweeting Behavior on Multiple Heterogeneous Diffusion Relation Networks (RBMHDRN) to predict user retweeting behavior in social networks. Experiments on a real dataset extracted from a social network site Weibo demonstrate the effectiveness of our proposed model, indicating that our generated features and proposed approach can significantly improve the performance of predicting user retweeting behavior occurring in the process of information diffusion in social networks.