Detecting Fake Review Buyers Using Network Structure: Direct Evidence from Amazon

Download This Paper Open PDF in Browser Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Detecting Fake Review Buyers Using Network Structure: Direct Evidence from Amazon 25 Pages Posted: 11 Jul 2022 See all articles by Sherry HeSherry HeUniversity of California, Los Angeles (UCLA), Anderson School of ManagementBrett HollenbeckUniversity of California, Los Angeles (UCLA) - Anderson School of ManagementGijs Overgooraffiliation not provided to SSRNDavide ProserpioMarshall School of Business, University of Southern CaliforniaAli TosyaliRIT Saunders College of Business Date Written: June 27, 2022 Abstract Online reviews have a significant impact on consumers decision-making process and firms economic outcomes, and are widely seen as crucial to the success of online markets. Due to being central to their economic outcomes, firms have a strong incentive to manipulate ratings using fake positive reviews. While academic researchers have spent two decades on this problem and platforms expend large resources on it, the prevalence of fake reviews is arguably higher than ever. We collect a dataset of reviews and ratings for thousands of Amazon products, and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using lab-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We start by showing that products that buy fake reviews are highly clustered in the product-reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews, and that models trained on this features outperforms methods not accounting for them. We then show that our network-based approach is also successful at detecting fake reviews even without ground truth data, as unsupervised clustering methods can identify fake review buyers accurately simply by identifying clusters of products that are closely connected in the network. While review text or metadata can be manipulated by sellers who have a strong incentive to evade detection, network-based features are very difficult to manipulate because they follow directly from the economics of fake review marketplaces, making network-based fake review detection approaches robust to manipulation. Keywords: Online reviews, machine learning, networks, text analysis JEL Classification: L51, M31, C55, C45 Suggested Citation: Suggested Citation He, Sherry and Hollenbeck, Brett and Overgoor, Gijs and Proserpio, Davide and Tosyali, Ali, Detecting Fake Review Buyers Using Network Structure: Direct Evidence from Amazon (June 27, 2022). Available at SSRN: https://ssrn.com/abstract=4147920 Sherry He University of California, Los Angeles (UCLA), Anderson School of Management ( email ) 110 Westwood PlazaB411Los Angeles, CA 90049United States Brett Hollenbeck (Contact Author) University of California, Los Angeles (UCLA) - Anderson School of Management ( email ) 110 Westwood PlazaLos Angeles, CA 90095-1481United States Gijs Overgoor affiliation not provided to SSRN Davide Proserpio Marshall School of Business, University of Southern California ( email ) 701 Exposition BlvdLos Angeles, CA Los Angeles 90089United States HOME PAGE: http://dadepro.github.io/ Ali Tosyali RIT Saunders College of Business ( email ) Rochester, NY 14623United States Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? 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