Authors: Jun Zhao; Hong Wang
Addresses: School of Information Science and Engineering, Shandong Normal University, Wenhua East Road No. 88, Jinan, Shandong Province, China ' School of Information Science and Engineering, Shandong Normal University, Wenhua East Road No. 88, Jinan, Shandong Province, China
Abstract: Online product reviews can greatly affect the consumer's shopping decision. Thus, a larger number of unscrupulous merchants post fake or unfair reviews to mislead consumers for their profit and fame. The common approaches to find these spam reviews are analysing the text similarity or rating pattern. With these common approaches we can easily identify ordinary spammers, but we cannot find the unusual ones who manipulate their behaviour to act just like genuine reviewers. In this paper, we propose a novel method to recognise these unusual ones by using relations among reviewers, reviews, commodities and stores. Firstly, we present four fundamental concepts, which are the quality of the merchandise, the honesty of the review, the trustworthiness of the reviewer and the reliability of the store, thus enabling us to identify the spam reviewers more efficiently. Secondly, we propose our multimode network model for identifying suspicious reviews and then give three corresponding algorithms. Eventually, we find that the multi-view spam detection based on the multimode network can detect more subtle false reviews according to our experiments.
Keywords: multi-mode network; multi-view detecting; multi-dimensional network; logistic regression; quality of commodities.
International Journal of High Performance Computing and Networking, 2019 Vol.13 No.4, pp.408 - 416
Received: 22 May 2016
Accepted: 15 Oct 2016
Published online: 24 Apr 2019 *