Rating Manipulation: Credibility Inversion and Audit Leakage

Abstract

Online ratings turn a long review history into one public number. This makes sellers easier to compare, but it also gives them a single clear target to manipulate. We study a low-quality seller who can add fake reviews carrying different scores, buyers who infer quality from the displayed average, and a platform that can target particular scores for enforcement. The model separates the rating buyers see from the hidden mix of reviews used to produce it, and the two need not move together. An almost-perfect rating can be less credible than a slightly lower one when low-quality sellers are especially likely to manufacture the top of the scale, so a seller whose buyers become more valuable may display less and sell more. At a fixed displayed rating, targeted enforcement can redirect fake reviews toward other scores rather than eliminate manipulation; buyers do not see this substitution because the displayed average is unchanged. Raw ratings therefore provide only a partial picture of credibility and enforcement, and buyer-oriented ranking should account for what a rating conveys, not only its numerical level.