A dynamic influence model of social network hotspot based on grey system

Abstract

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 viewpoint 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.