Who Will Tweet More? Finding Information Feeders in Twitter
Twitter is an important source of information to users for its giant user group and rapid information diffusion but also made it hard to track topics in oceans of tweets.Such situation points the way to consider the task of finding information feeders,a finer-grained user group than domain experts.Information feeders refer to a crowd of topic tracers that share interests in a certain topic and provide related and follow-up information.In this study,we explore a wide range of features to find Twitter users who will tweet more about the topic after a time-point within a machine learning framework.The features are mainly extracted from the users history tweets for that we believe users tweet decision depends most on his history activities.We considered four feature families: activeness,timeliness,interaction and user profile.From our results,activeness in users history data is most useful.Besides that,we concluded people who gain social influence and make quick response to the topic are more likely to post more topic-related tweets.
Beibei Gu Zhunchen Luo Xin Wang
China Defense Science and Technology Information Center
国际会议
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
昆明
英文
1-11
2016-12-02（万方平台首次上网日期，不代表论文的发表时间）