Predicting User Mention Behavior in Online Social Networks
Mentioning is a social function used to explicitly point target users for specific information in online social networks(OSNs).Understanding user mention behavior can provide important insights into questions of human social behavior and improve design of social network platforms.However,most previous work mainly focus on mention network for the effect of information diffusion,few researches consider the link prediction problem of user mention behavior.In this paper,we propose an intuitive approach to predict user future mentions using link prediction method in OSNs.Specifically,we first formulate user mention prediction problem as a supervised classification task,and then extract new features including interest match,social influence and mention frequency 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.
social network mining user mentions link prediction supervised classification
Bo Jiang Ying Sha
Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China
国内会议
武汉
英文
444-453
2015-05-26（万方平台首次上网日期，不代表论文的发表时间）