Topic Influence Analysis Based on User Intimacy and Social Circle Difference

Analyzing topical user influence in online social networks is conducive to better advertisement injection, information dissemination, and user behavior analysis. In this paper, we propose a new approach to measure topical user influence in online social networks. Specifically, by comprehensively considering users’ social relationships, posting and forwarding behaviors, and posts content, we define two metrics of user intimacy and social circle difference to measure how influential users rank on different topics. The dataset obtained from Sina Weibo is utilized to evaluate the effects of our proposed approach and several comparison methods. Specifically, the advantages of our approach are evaluated from several aspects, including the correlations between influence rankings on different topics, the spread ability of high influential users, and the spread balance of high influential users. The extensive experimental results show that our approach is superior in mining influential users on different topics. Specifically, in our approach, the user influence rankings on different topics are less correlated, the users with high influence rankings achieve higher spread scope, and the higher a user’s influence ranking, the more evenly the user’s posts being spread among different communities.