Identifying influential reviewers for word-of-mouth marketing

Authors:
Yung-Ming Li;Chia-Hao Lin;Cheng-Yang Lai
Affiliations:
Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan;Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan
Venue:
Electronic Commerce Research and Applications
Year:
2010

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Abstract

The key to word-of-mouth marketing is to discover the potential influential nodes for efficiently spreading product impressions. In this paper, a framework combined with mining techniques, a modified PMI measure, and an adaptive RFM model is proposed to evaluate the influential power of online reviewers. An artificial neural network is adopted to identify the target reviewers and a well-developed trust mechanism is utilized for effectiveness evaluation. This proposed framework is verified by the data collected from Epinions.com, one of the most popular online product review websites. The experimental results show that the proposed model could accurately identify which reviewers to select to become the influential nodes. This proposed approach can be exploited in effectively carrying out online word-of-mouth marketing, which can save a lot of resources in finding customers.