Exploring Multifeatures for Retweet Prediction on Social Networks
Retweet behavior is the key mechanism for information diffusion on microblogging networks.Mining and understanding the latent mechanism of retweet behavior is important for predicting,controlling information propagation.In this paper,we firstly do some empirical analysis on both user,weibo,interaction and user-centered transfer networks.Base on the result of correlation,we choose user attributes,microblog content,interactive attributes and local structures as main features for predicting user”s retweet behavior.Comparing with other studies,our method takes into account not only users” recent situation,such as user”s retweet activity,interactive strength and interests,but also users” near neighbors influence and the diversity of local structures.Then,using these multifeatures,we utilize some different fashion supervised classifiers to predict retweet behavior on Sina Weibo dataset.The experiments and evaluation show the effectiveness of our feature choices.And the results show that the features we selected combined with the Logistic Regression model for predicting user”s retweet behavior more accurately.
multifeatures retweet behavior prediction social networks
Min Liu Li Wang
College of Computer Science and Technology,Taiyuan University of Technology,China
国内会议
第10届全国计算机支持的协同工作学术会议暨中国计算机学会协同计算专委年度工作会议
太原
英文
520-527
2015-08-28（万方平台首次上网日期，不代表论文的发表时间）