An Inconvenient Truth : Are Platforms Reliable in Tackling Fake Reviews?

E-commerce platforms worldwide have faced the enduring challenge of moderating fake reviews over the past decade. Hence, in this paper, we investigate the platforms’ economic incentives to remove fake reviews, which is an important but unanswered question. Using a game-theoretic model, we analyze the sellers’ review manipulation behavior and the platform’s optimal fake review control strategy. Our results show that a more accurate detection algorithm can sometimes lead to a lower payoff for the platform, and in the equilibrium, the platform may not set the accuracy level to its maximum value even when it is costless to do so. Another interesting finding is that the high-quality seller can sometimes set a higher manipulation level than the low-quality seller, especially when the competition is intense. Moreover, we find that the optimal algorithm accuracy increases when the platform is more reputable and competition among sellers is weakened. In addition to these results, we present several other interesting insights in the paper that could help platforms, sellers, as well as policymakers, in devising appropriate strategies. Our insights send an important message to consumers and policymakers that they should not fully rely on the platform to tackle the fake review problem