Discovering target groups in social networking sites: An effective method for maximizing joint influential power

  • Authors:

  • Kaiquan Xu;Xitong Guo;Jiexun Li;Raymond Y. K. Lau;Stephen S. Y. Liao

  • Affiliations:

  • Department of Electronic Business, School of Business (Management), Nanjing University, PR China;School of Management, Harbin Institute of Technology, PR China;College of Information Science and Technology, Drexel University, USA;Department of Information Systems, City University of Hong Kong, Hong Kong;Department of Information Systems, City University of Hong Kong, Hong Kong

  • Venue:
  • Electronic Commerce Research and Applications
  • Year:
  • 2012

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Abstract

With the tremendous popularity of social networking sites in this era of Web 2.0, increasingly more users are contributing their comments and opinions about products, people, organizations, and many other entities. These online comments often have direct influence on consumers' buying decisions and the public's impressions of enterprises. As a result, enterprises have begun to explore the feasibility of using social networking sites as platforms to conduct targeted marking and enterprise reputation management for e-commerce and e-business. As indicated from recent marketing research, the joint influential power of a small group of active users could have considerable impact on a large number of consumers' buying decisions and the public's perception of the capabilities of enterprises. This paper illustrates a novel method that can effectively discover the most influential users from social networking sites (SNS). In particular, the general method of mining the influence network from SNS and the computational models of mathematical programming for discovering the user groups with max joint influential power are proposed. The empirical evaluation with real data extracted from social networking sites shows that the proposed method can effectively identify the most influential groups when compared to the benchmark methods. This study opens the door to effectively conducting targeted marketing and enterprise reputation management on social networking sites.