Influential Online Forum User Detection Based on User Contribution and Relevance

Abstract

With the development of the automated facilitation support for online forum, influential user detection becomes a critical issue for supporting human facilitator. Influential maximization (IM) aiming at choosing a set of users that maximize the influence propagation from the entire social network users is one of the key approaches to detect influential users in online social network. However, conventional IM algorithms cannot be applied to online forum because of the lack of existing social network. In addition, they neglect many real-world factors such as the characteristics of individual users and relation between users that should be considered in online forums influential user detection. In this paper, we propose a novel IM-based approach to detect influential users in online forum. The online forum influence propagation network (OFIPN) is modeled with the consideration of both individual contribution and relevance between users, and a heuristic algorithm that aims to find influential users in OFIPN is proposed. Experiments are conducted by utilizing a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.