Fake Reviews

Abstract

We propose a model of product reviews in which some are genuine and some are fake in order to shed light on the value of information provided on platforms like TripAdvisor, Yelp, etc. In every period, a review is posted which is either genuine or fake. We characterize the equilibrium of the dynamic model and prove that it is unique. In equilibrium, valuable learning takes place in every period. Fake reviews, however, do slow down the learning process. It is established that any attempt by the platform to manipulate the reviews is counterproductive.

Fake Reviews in Online Platforms and the Effort to Fight Them

“…On the one hand, our study is related to previous studies that analysed strategic advertising for firms competing in oligopolistic markets using both static (Dukes, 2004;Hamilton, 2009) and dynamic economic models (Cellini & Lambertini, 2002;Erickson, 2009), and, more specifically, to studies investigating misleading advertising (Hattori & Higashida, 2012;Matsumura & Sunada, 2013). On the other hand, our study is also connected to previous research about fake online reviews (for a review, see Wu et al, 2020) and, more specifically, to the growing literature in Economics analysing fake reviews on online platforms from a theoretical perspective (Chen et al, 2109;Glazer et al, 2021;Knapp, 2021). Our study extends previous research by proposing a theoretical model that incorporates the optimal level of effort that an online platform may exert to fight fake reviews and its interplay with online sellers' behaviour.…”

In Vi(vi)no Veritas? Expertise, Review Accuracy and Reputation Inflation

“…Filippas et al (2022) show that the share of workers with a perfect 5-star rating on an online marketplace for labor grew from 33 percent to 85 percent in only six years and describe similar patterns for other platforms. One potential explanation for the overwhelmingly positive majority of reviews is a large and increasing share of fake reviews (Glazer et al, 2021;He et al, 2022;Luca and Zervas, 2016). Beyond an increasing number of fake or inauthentic reviews, bias within the pool of reviewers or within the review content could decrease the ability of these reviews to reduce information asymmetries.…”

Influencer Cartels

“…We study influencer cartels where groups of influencers collude to increase each others' indicators of social media influence. While there is substantial literature in economics on fake consumer reviews (Mayzlin et al, 2014;Luca and Zervas, 2016;He et al, 2022;Glazer et al, 2021;Smirnov and Starkov, 2022) and other forms of advertising fraud (Zinman and Zitzewitz, 2016;Rhodes and Wilson, 2018), the economics of this fraudulent behavior has not been studied.…”