Abstract
ABSTRACT Fake reviews have become an increasingly relevant issue in electronic commerce. Regulators and platform operators have begun to take action against fake reviews through regulation and litigation. While there has emerged an ample body of research on fake reviews in open reputation systems (i.e. where everyone can write reviews), little is known about the frequency and nature of fake reviews in closed reputation systems (i.e. where the completion of a transaction is a necessary precondition for reviewing). This article assesses the frequency of fake reviews in this particular domain of the platform economy. To do so, we draw on data from Airbnb and investigate systematic differences between first and second rating score distributions, estimating the proportion of potentially fake five-star ratings using KL-divergence. In doing so, we advance the understanding of fake reviews in closed reputation systems, offering theoretical insights into review dynamics and practical strategies for detecting fraudulent behavior using score distributions and user attributes. This shows that early-on reputation is crucial for complementors to overcome the cold-start problem, and platform operators will have to think of ways to allow them to do so (e.g. reputation imports) in order not to jeopardize the overall credibility of their reputation systems.
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