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Online reviews have a significant impact on consumer purchase decisions and are widely seen as crucial to the success of online markets (1, 2). Review and rating systems allow buyers and sellers to develop credible reputations in settings that are otherwise mostly anonymous. Because these reputations are crucial for seller outcomes, sellers have a large incentive to manipulate their ratings and inflate their reputation. As a result, online review platforms like Amazon, Yelp, or Tripadvisor have struggled since their inception with the problem of sellers manipulating their ratings with fake reviews. Rating manipulation can potentially cause buyers to buy from lower-quality sellers than they otherwise would, allow sellers to charge higher prices than if their true reputation was observed, and lower trust in reviews and review platforms, making it difficult for high-quality and honest sellers to compete. There is growing empirical evidence that fake reviews harm consumers (3). In addition to violating the platform's policies, these practices are the subject of ongoing investigations by the Federal Trade Commission, the United Kingdom Competition and Markets Authority, and other regulators. Identifying Which Products Buy Fake Reviews share photo Share of reviews with review photo Top-two network 0.879 0.812 0.832 0.787 0.812 Fig. 2 shows the relative importance of different individual features from the all-feature model in terms of their contribution to predictive power. The two most important features, by a large margin, are network features, specifically, the clustering coefficient and the eigenvector centrality score. The fact that these two features rank highest in importance highlights the reason why the network-based model performs so well. Products buying fake reviews appear to rely on a common set of reviewers, causing them to be more closely clustered in the product network compared to regular products. Fig. 3 shows the percentage and the total number of products identified as buying fake reviews in each cluster, using our classifier. We report summary statistics of clusters and the details of the unsupervised approach in SI Appendix, section 4. Go to: 15. Kumar N., Venugopal D., Qiu L., Kumar S., Detecting review manipulation on online platforms with hierarchical supervised learning. J. Manage. Inf. Syst. 35, 350–380 (2018). [Google Scholar] crossbody bags cyber monday