在意見分享社群中評論者聲譽預測之研究

Predicting Reputation of Reviewers on the Opinion-Sharing Communities

陳翊莎(Luisa Angelica Chen Ng)

指導教授 ： 魏志平

國立清華大學/科技管理學院/科技管理研究所/碩士(2007年)

摘要

Online communities allowing users to express personal opinions and preferences (e.g., products or product features they like or dislike) are becoming increasingly popular in recent years. However, due to the openness and anonymity of opinion-sharing communities, users also face a very challenging issue; that is, whether to believe or disbelieve information asserted by other users in the community. Therefore, it is desirable to develop an effective mechanism to better facilitate users’ information search and browsing process in online communities. In response, the purpose of this study is to develop a data-mining-based reputation prediction approach for predicting reputation of reviewers in opinion-sharing communities. The prediction of reputation scores of members in an opinion-sharing community can help users to easily find reputable reviewers in the community and can facilitate users to judge whether to believe or disbelieve reviews written by different reviewers in the community. In this study, we identify fourteen independent variables related to review, rating, and trust activities/behaviors of members and apply M5 and SVM Regression as our underlying learning algorithm. Our empirical evaluation results on the basis of four product categories suggest that our proposed approach can satisfactorily predict reputation scores of members in opinion-sharing communities. In addition, M5 appears to be more effective than SVM Regression. Our empirical evaluation also shows that trust-related variables play important roles in predicting reputation scores of members in an opinion-sharing community.

關鍵字

Reputation prediction ； Opinion-sharing community ； Data mining, ； M5 ； SVM regression

並列摘要

Online communities allowing users to express personal opinions and preferences (e.g., products or product features they like or dislike) are becoming increasingly popular in recent years. However, due to the openness and anonymity of opinion-sharing communities, users also face a very challenging issue; that is, whether to believe or disbelieve information asserted by other users in the community. Therefore, it is desirable to develop an effective mechanism to better facilitate users’ information search and browsing process in online communities. In response, the purpose of this study is to develop a data-mining-based reputation prediction approach for predicting reputation of reviewers in opinion-sharing communities. The prediction of reputation scores of members in an opinion-sharing community can help users to easily find reputable reviewers in the community and can facilitate users to judge whether to believe or disbelieve reviews written by different reviewers in the community. In this study, we identify fourteen independent variables related to review, rating, and trust activities/behaviors of members and apply M5 and SVM Regression as our underlying learning algorithm. Our empirical evaluation results on the basis of four product categories suggest that our proposed approach can satisfactorily predict reputation scores of members in opinion-sharing communities. In addition, M5 appears to be more effective than SVM Regression. Our empirical evaluation also shows that trust-related variables play important roles in predicting reputation scores of members in an opinion-sharing community.

參考文獻

Anderson, D.R., Sweeney, D. J., and Williams, T.A. (2002). Statistics for Business and Economics. Mason, Ohio. Southern-Western.

Chen, M. and Singh, J.P. (2001). “Computing and Using Reputations for Internet Ratings,” Proceedings of Third ACM Conference on Electronic Commerce.

Coppola, N. (1999). “Setting the Discourse Community: Tasks and Assessment for the New Technical Communication Service Course,” Technical Communication Quarterly, Vol. 8, No. 3, pp. 249-268.

Dellarocas, C. (2003). “The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms,” Management Science, Vol. 49, No. 10, pp. 1407-1424.

Grabner-Krauter, S. and Kaluscha, E.A. (2003). “Empirical Research in Online-Trust: A Review and Critical Assessment,” International Journal of Human-Computer Studies, Vol. 58, No. 6, pp.783-812.