A cluster-based opinion leader discovery in social network

Abstract

Recently, opinion leader discovery has drawn much attention due to its widespread applicability. By identifying the opinion leader, companies or governments can manipulate the selling or guiding public opinion, respectively. However, mining opinion leader is a challenge task because of the complexity of processing social graph and analyzing leadership quality. In this study, a novel method, TCOL-Miner, is proposed to efficiently find the opinion leaders from a huge social network. We integrate the clustering and semantic analysis methods with some pruning strategies to tackle the influence overlapping issue and the potential leadership of individuals. The experimental results show that the proposed TCOL-Miner can effectively discover the influenced opinion leaders in different real social networks with efficiency.