Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
复制标题
DOI：
10.1287/mnsc.2015.2304
复制
发表时间：
2016-12-01
影响因子：
5.4
通讯作者：
Zervas, Georgios
中科院分区：
文献类型：
--
作者：
Luca, Michael;Zervas, Georgios
消费者评论现在是日常决策的一部分。然而，当企业进行评论欺诈时，这些评论的可信度从根本上受到破坏，为自己或竞争对手创造虚假评论。我们使用两种互补的方法和数据集，研究了在流行的评论平台Yelp上进行评论欺诈的经济动机。我们开始分析被Yelp的过滤算法识别为可疑或虚假的餐馆评论，并将其视为评论欺诈的代理（我们提供证据的假设）。我们提出了四个主要发现。首先，Yelp上大约16%的餐馆评论被过滤。这些评论往往比其他评论更极端（有利或不利），并且随着时间的推移，可疑评论的流行程度显着增加。其次，当餐馆的声誉较弱时，它更有可能犯下评论欺诈行为，即，当它有很少的评论或它最近收到了差评。第三，连锁餐厅从Yelp中获益较少，也不太可能犯评论欺诈罪。第四，当餐馆面临日益激烈的竞争时，他们更有可能收到不利的虚假评论。使用一个单独的数据集，我们分析了通过Yelp进行的诱骗而被发现征求虚假评论的企业。这些数据支持我们的主要结果，并进一步揭示了企业决定留下虚假评论背后的经济动机。
Consumer reviews are now part of everyday decision making. Yet the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit review fraud on the popular review platform Yelp, using two complementary approaches and data sets. We begin by analyzing restaurant reviews that are identified by Yelp's filtering algorithm as suspicious, or fake-and treat these as a proxy for review fraud (an assumption we provide evidence for). We present four main findings. First, roughly 16% of restaurant reviews on Yelp are filtered. These reviews tend to be more extreme (favorable or unfavorable) than other reviews, and the prevalence of suspicious reviews has grown significantly over time. Second, a restaurant is more likely to commit review fraud when its reputation is weak, i.e., when it has few reviews or it has recently received bad reviews. Third, chain restaurants-which benefit less from Yelp-are also less likely to commit review fraud. Fourth, when restaurants face increased competition, they become more likely to receive unfavorable fake reviews. Using a separate data set, we analyze businesses that were caught soliciting fake reviews through a sting conducted by Yelp. These data support our main results and shed further light on the economic incentives behind a business's decision to leave fake reviews.