Quarantine Deceiving Yelp's Users by Detecting Unreliable Rating Reviews

• URL: http://arxiv.org/abs/2004.09721v1
• Date: Tue, 21 Apr 2020 02:44:10 GMT
• Title: Quarantine Deceiving Yelp's Users by Detecting Unreliable Rating Reviews
• Authors: Viet Trinh, Vikrant More, Samira Zare, and Sheideh Homayon
• Abstract summary: We focus on quarantining Yelp's users that employ both review spike detection (RSD) algorithm and spam detection technique in bridging review networks (BRN) We found that more than 80% of Yelp's accounts are unreliable, and more than 80% of highly-rated businesses are subject to spamming.
• Score: 1.3999481573773074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online reviews have become a valuable and significant resource, for not only consumers but companies, in decision making. In the absence of a trusted system, highly popular and trustworthy internet users will be assumed as members of the trusted circle. In this paper, we describe our focus on quarantining deceiving Yelp's users that employ both review spike detection (RSD) algorithm and spam detection technique in bridging review networks (BRN), on extracted key features. We found that more than 80% of Yelp's accounts are unreliable, and more than 80% of highly-rated businesses are subject to spamming.

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