The outbreak of hotspot in social network may contain complex dynamic genesis. Using user behavior data from hotspots in social network, we study how different user groups play different roles for a hotspot topic. Fir...The outbreak of hotspot in social network may contain complex dynamic genesis. Using user behavior data from hotspots in social network, we study how different user groups play different roles for a hotspot topic. Firstly, by analyzing users' behavior records, we mine group situation that promotes the hotspot.Several major attributions in a hotspot outbreak, such as individual, peer and group triggers, are defined formally according to the view-point of social identity, social interaction, retweet depth and opinion leader. Secondly,for the problem of the uneven and sparse data in each stage of hotspot topic's life cycle, we propose a dynamic influence model based on grey system to formalize the effect of different groups. Then the process of hotspot evolution driven by distinct crowd is showed dynamically. The experimental result confirms that the model is able not only to qualify users' influence on a hotspot topic but also to predict effectively an upcoming change in a hotspot topic.展开更多
基金supported by National Basic Research Program of China(973 program)(Grant No.2013CB3296-06)National Natural Science Foundation of China(Grant No.61272400)+6 种基金Chongqing Innovative Team Fund for College Development Project(Grant No.KJTD201310)Chongqing Youth Innovative Talent Project(Grant No.cstc2013kjrc-qnrc40004)Ministry of Education of China and China Mobile Research Fund(Grant No.MCM20130351)Science and Technology Research Program of the Chongqing Municipal Education Committee(Grant No.KJ1500425)Wen Feng Foundation of CQUPT(Grant No.WF201403)Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Open Subject(Grant No.ITD-U13002/KX132600009)Chongqing Graduate Research and Innovation Project(Grant No.CYS14146)
文摘The outbreak of hotspot in social network may contain complex dynamic genesis. Using user behavior data from hotspots in social network, we study how different user groups play different roles for a hotspot topic. Firstly, by analyzing users' behavior records, we mine group situation that promotes the hotspot.Several major attributions in a hotspot outbreak, such as individual, peer and group triggers, are defined formally according to the view-point of social identity, social interaction, retweet depth and opinion leader. Secondly,for the problem of the uneven and sparse data in each stage of hotspot topic's life cycle, we propose a dynamic influence model based on grey system to formalize the effect of different groups. Then the process of hotspot evolution driven by distinct crowd is showed dynamically. The experimental result confirms that the model is able not only to qualify users' influence on a hotspot topic but also to predict effectively an upcoming change in a hotspot topic.
