Identifying online opinion leaders using K-means clustering

Online opinion leaders play an important role in the dissemination of information in discussion forums. They are a high-priority target group for viral marketing campaigns. On an average, an opinion leader will tell about his or her experience with a product or company to 14 other people. It is important to identify such opinion leaders from data derived from online activity of users. We present an approach to identification of opinion leaders using the K-means clustering algorithm. This approach does not require knowledge of the user's opinions or membership in other forums.

[1]  Xindong Wu,et al.  The Top Ten Algorithms in Data Mining , 2009 .

[2]  Carlo Vercellis,et al.  Business Intelligence: Data Mining and Optimization for Decision Making , 2009 .

[3]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[4]  Yun Chi,et al.  Identifying opinion leaders in the blogosphere , 2007, CIKM '07.

[5]  J. Kirby,et al.  Connected Marketing: The Viral, Buzz And Word Of Mouth Revolution , 2007 .

[6]  Freimut Bodendorf,et al.  Detecting Opinion Leaders and Trends in Online Communities , 2010, 2010 Fourth International Conference on Digital Society.

[7]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[8]  Hua Xu,et al.  Identifying Opinion Leaders in BBS , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.