Mining Opinion Leaders in Big Social Network

• Published in: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA) pp. 1012 - 1018
• Main Authors: Yi-Cheng Chen, Yi-Hsiang Chen, Chia-Hao Hsu, Hao-Jun You, Jianquan Liu, Xin Huang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2017
• Subjects: Algorithm design and analysis; clustering; Clustering algorithms; Companies; Computers; Heuristic algorithms; Mathematical model; opinion leader; semantic analysis; social network; Social network services
• DOI: 10.1109/AINA.2017.147
• ISSN: 1550-445X

Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion leaders in a huge social network is a challenge task because of the complexity of graph processing and leadership analysis. In this study, a novel algorithm, OLMiner, is proposed to efficiently find the opinion leaders from a huge social network. We propose a clustering method to solve the influence overlapping issue and significantly reduce the computation time by shrinking the size of candidate generation. The experimental results show that the proposed OLMiner can effectively discover the influential opinion leaders in different real social networks with efficiency.