Detecting Local Opinion Leader in Semantic Social Networks: A Community-Based Approach

Abstract

Online social networks have been incorporated into people’s work and daily lives as social media and services continue to develop. Opinion leaders are social media activists who forward and filter messages in mass communication. Therefore, competent monitoring of opinion leaders may, to some extent, influence the spread and growth of public opinion. Most traditional opinion leader mining approaches focus solely on the user’s network structure, neglecting the significance and role of semantic information in the generation of opinion leaders. Furthermore, these methods rank the influence of users globally and lack effectiveness in detecting local opinion leaders with low influence. This paper presents a community-based opinion leader mining approach in semantic social networks to address these issues. Firstly, we present a new node semantic feature representation method and community detection algorithm to generate the local public opinion circle. Then, a novel influence calculation method is proposed to find local opinion leaders by combining the global structure of the network and local structure of the public opinion circle. Finally, nodes with high comprehensive influence are identified as opinion leaders. Experiments on real social networks indicate that the proposed method can accurately measure global and local influence in social networks, as well as increase the accuracy of local opinion leader mining.