Tweets Ranking Considering Dynamic Social Influence and Personal Interests

Abstract

Social networks play important roles in information propagation and interactions among friends. Billions of information stream are created by users in Twitter and diffuse among relationship networks. Users may receive mass tweets posted by their followees every week. Thus, how to rank and display the tweets users are interested in and willing to interact with, is necessary to improve user's experience. Information propagation is usually driven by user interaction behaviors including retweet and comment. While these behaviors are mainly affected by the social influence and user interest, which dynamically change with time. In this paper, we propose a tweets ranking model to predict information diffusion based on dynamic social influence and personal interests. Our model combines these factors together by exploiting listwise algorithm such as ListNet. Experiments show that our model performs better than several baseline models, and both social influence and personal interests play important roles in tweets ranking.