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

26 Pages Posted: 11 Jul 2022 Last revised: 1 May 2023

See all articles by Sherry He

Sherry He

University of California, Los Angeles (UCLA), Anderson School of Management

Brett Hollenbeck

University of California, Los Angeles (UCLA) - Anderson School of Management

Gijs Overgoor

Rochester Institute of Technology

Davide Proserpio

Marshall School of Business - University of Southern California

Ali Tosyali

RIT Saunders College of Business

Date Written: June 27, 2022

Abstract

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews 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 show 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. We show that our network-based approach is also successful at detecting fake reviews even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.

Keywords: Online reviews, machine learning, networks, text analysis

JEL Classification: L51, M31, C55, C45

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). USC Marshall School of Business Research Paper Sponsored by iORB, Available at SSRN: https://ssrn.com/abstract=4147920 or http://dx.doi.org/10.2139/ssrn.4147920

Sherry He

University of California, Los Angeles (UCLA), Anderson School of Management ( email )

110 Westwood Plaza
B411
Los Angeles, CA 90049
United States

Brett Hollenbeck (Contact Author)

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Gijs Overgoor

Rochester Institute of Technology ( email )

105 Lomb Memorial Dr.
Rochester, NY 14623
United States

Davide Proserpio

Marshall School of Business - University of Southern California ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

HOME PAGE: http://dadepro.github.io/

Ali Tosyali

RIT Saunders College of Business ( email )

Rochester, NY 14623
United States

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