A Multi-view Retweeting Behaviors Prediction in Social Networks

Abstract

Retweeting is the most prominent feature in online social networks. It allows users to reshare another user’s tweets for her followers and bring about second information diffusion. Predicting retweeting behaviors is an important and essential task for advertising product launch, hot event detection and analysis of human behavior. However, most of the methods and systems have been developed for modeling the retweeting behaviors, it has not been fully explored for this problem. In this paper, we first cast the problem of retweeting behaviors prediction as a classification task and propose a formally definition. We then systematically summarize and extract a lot of features, namely user status, content, temporal, and social tie information, for predicting users’ retweeting behaviors. We incorporate these features into Support Vector Machine (SVM) model for our prediction problem. Finally, we conduct extensive experiments on a real world dataset collected from Twitter to validate our proposed approach. Our experimental results demonstrate that our proposed model can improve prediction effectiveness by combining the extracted features compared to the baselines that do not.