Identifying Influencers in Online Social Networks: The Role of Tie Strength

Yifeng Zhang, Xiaoqing Li and Te-Wei Wang
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Yifeng Zhang: Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA
Xiaoqing Li: Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA
Te-Wei Wang: Department of Management Information Systems, University of Illinois at Springfield, Springfield, Illinois, USA

International Journal of Intelligent Information Technologies (IJIIT), 2013, vol. 9, issue 1, 1-20

Abstract: Online social networks (OSNs) are quickly becoming a key component of the Internet. With their widespread acceptance among the general public and the tremendous amount time that users spend on them, OSNs provide great potentials for marketing, especially viral marketing, in which marketing messages are spread among consumers via the word-of-mouth process. A critical task in viral marketing is influencer identification, i.e. finding a group of consumers as the initial receivers of a marketing message. Using agent-based modeling, this paper examines the effectiveness of tie strength as a criterion for influencer identification on OSNs. Results show that identifying influencers by the number of strong connections that a user has is superior to doing so by the total number of connections when the strength of strong connections is relatively high compared to that of weak connections or there is a relatively high percentage of strong connections between users. Implications of the results are discussed.

Date: 2013
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