Abstract
A number of methods have been proposed for detecting spam reviews in order to obtain credible summaries. These methods, however, could not be uniformly applied to various forms of reviews and are not suitable for a product or service which has been evaluated by few reviewers. In this paper, we propose a bipartite graph model of review sites and a mutually reinforcing method of summarizing evaluations and detecting anomalous reviewers. Our model and method can be applied to reviews of various forms, and is suitable for a subject with few reviewers. We ascertain the effectiveness of our method using reviews of three forms on Yahoo! Movie web site. © 2011 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Tawaramoto, K., Kawamoto, J., Asano, Y., & Yoshikawa, M. (2011). A bipartite graph model and mutually reinforcing analysis for review sites. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6860 LNCS, pp. 341–348). https://doi.org/10.1007/978-3-642-23088-2_25
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