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Technical Perspective: A Rare Glimpse of Tracking Fake Reviews

balloon from smartphone shows question marks on five stars, illustration

The success of marketplaces such as Amazon is inextricably tied to the third-party sellers who compete on these platforms. As e-commerce platforms have grown in complexity, ratings and reviews have played a crucial role in consumer decision making. According to a Pew Research survey, 93% of Americans read online reviews (sometimes) before purchase. However, the prominent role played by reviews has an unintended consequence: namely, it incentivizes sellers to purchase fake reviews to gain a competitive edge. These fake "5-star reviews" not only deceive consumers into buying low-quality products, they also trick the platform's algorithms into providing poor search results. In the long run, this can erode public trust in online markets.

Although academics and practitioners both agree on the dangers posed by fake reviews, there is little consensus on their severity and how to address the problem. For example, while Amazon claims fewer than 1% of reviews on the platform are inauthentic, independent monitors argue the number is larger than 20%. Gaining a better understanding of the ecosystem in which fake reviewers operate is a crucial step toward tackling fraud. Efforts in this direction, however, have been stymied by a lack of transparency (and data) into how platforms operate and how bad actors solicit fake reviews.


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