Integrating Ego, Homophily, and Structural Factors to Measure User Influence in Online Community

Research problem: In the current information age, people are increasingly accustomed to sharing their special interests online and are influenced by the relationships developed from that sharing. The purpose of this study was to better measure peer influence in these online communities. Research questions: 1. How can peer influence in online communities be measured in a way that comprehensively incorporates peer-based characteristics, the homophily effect, and the structural position of a user in the network? 2. Is the method proposed in this study superior to other existing methods? Literature review: Previous literature on measuring online user influence can be classified into two streams: 1. Those that focus on the intrinsic characteristics of social media players to measure peer influence; 2. Those that address social network structure. Relevant computing algorithms include Topic-Based PageRank, Quality-Structure index, and so on. Although the first stream considers afocal peer's intrinsic characteristics, it overlooks the interpeer attraction in terms of similarity and discrepant knowledge among peers. The second stream mostly stresses the structures of social networks to measure network-wide peer influence but underestimates the effect of interpeer attraction that may leverage every diffusion step of peer influence through the network. To fill this research gap, this study proposes a new method of measuring network user influence that incorporates peers' intrinsic factors, interpeer influence factors as homophily effect, and network structure. Homophily refers to the degree to which pairs of individuals who interact are similar with respect to certain attributes. Methodology: From the communication sender-receiver perspective, we developed a computable method that incorporates peer-based characteristics, the homophily effect, and the structural position of a user in the network to measure the social network user influence. Two empirical studies were subsequently conducted in a social network service-based online community and an online professional logistics community to verify the effectiveness of the proposed method. Results and conclusions: The empirical results show that our proposed method provides higher prediction accuracy of user influence rank in an online community than the other existing methods. These findings lay a foundation for future theoretical exploration and provide a useful tool for targeting influential users in online communitie