Incentives to Fake Reviews in Online Platforms

Abstract

With the proliferation of online rating platforms, there has been an increasing concern over the authenticity of reviews posted online. This paper develops a theoretical framework to study sellers' incentives to solicit fake reviews in online rating platforms, and provides empirical evidence supporting some of the model's conclusions. The model predicts that sellers' optimal investment in fake reviews is not a monotone function of their reputation, with sellers with either a very good or very bad history of past reviews displaying less incentives to fake reviews. Another prediction from the model is that, in order to maximize the impact from each fake review, sellers tend to concentrate review manipulation at the initial stages following their entrance (or reentered with a new name) into the market. Using data collected from Amazon, I was able to observe those features from the model at the empirical level by estimating the probability of a review being fake as a function of the product's reputation and the time it took for the review to be posted since the seller entered the market.