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Quarantine Deceiving Yelp's Users by Detecting Unreliable Rating Reviews

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.

preprint2020arXivOpen access
Viet TrinhVikrant MoreSamira ZareSheideh Homayon
Social and Information NetworksInformation RetrievalMachine Learning
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Open access4 authors3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Viet TrinhVikrant MoreSamira ZareSheideh Homayon

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