User Behaviour Network Based User Role Mining of Web Event

Abstract

With the fast growing of social media used in our society, user role mining, as one of the most important research domains of social media analysis, attracts more and more researchers' attention. Its research results can be applied to all walks of life, e.g., recommendation system, viral marketing, etc. Lots of researchers have presented many methods to mine user roles. However, most of the existing methods just analyse the user influence rather than mine user role. Therefore, user behaviour network based user role mining method of web event is proposed. User behaviour network is firstly built. Four network topologies (e.g., degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality) are used as the basis to measure users, and combining number of comment, number of repost, and statistical characteristic to mine three different user roles (information producer, information driver, and information bridger) of web event. Experimental results on the Weibo datasets show the effectiveness of the proposed model.