Mining Opinion Leaders in Big Social Network

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...

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Bibliographic Details
Published in2017 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
LanguageEnglish
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
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Summary: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.
ISSN:1550-445X
2332-5658
DOI:10.1109/AINA.2017.147