Controlling Fake Reviews

In this study, I theoretically analyze fake reviews on a platform market using models where a seller creates fake reviews through incentivized transactions, and its sales depend on its rating based on a review history. The platform can control the incentive for fake reviews by changing the parameters of the rating system, such as its filtering policy and weights, for past reviews. At equilibrium, the number of fake reviews increases as quality increases but decreases as reputation improves. Since fake reviews have a positive relationship with a product’s underlying quality, under some parameters, rational consumers find a rating more informative when fake reviews exist, while credulous consumers suffer from a bias caused by boosted reputation. A stringent filtering policy can decrease the expected amount of fake reviews and the bias of credulous consumers, but at the same time, it can decrease the informativeness of a rating system for rational consumers. In terms of the weight placed on the review history, rational consumers benefit from higher weights on past reviews than from optimal weights without fake reviews.