Identification of influential users based on topic-behavior influence tree in social networks

Abstract

Identifying influential users in social networks is of significant interest, as it can help improve the propagation of ideas or innovations. Various factors can affect the relationships and the formulation of influence between users. Although many studies have researched this domain, the effect of the correlation between messages and behaviors in measuring users’ influence in social networks has not been adequately focused on. As a result, influential users can not be accurately evaluated. Thus, we propose a topic-behavior influence tree algorithm that identifies influential users using six types of relationships in the following factors: message content, hashtag titles, retweets, replies, and mentions. By maximizing the number of affected users and minimizing the propagation path, we can improve the accuracy of identifying influential users. The experimental results compared with state-of-the-art algorithms on various datasets and visualization on TUAW dataset validate the effectiveness of the proposed algorithm.