估计在线评论社区中的欺骗流行率
Estimating the Prevalence of Deception in Online Review Communities
摘要：
消费者的购买决定越来越受到用户生成的在线评论的影响。因此，人们越来越担心发布“欺骗性意见垃圾邮件”的可能性-故意写的虚假评论听起来真实，欺骗读者。但是，虽然这种做法已经收到了相当大的公众关注和关注，相对较少的是知道的实际患病率，或率，欺骗在网上评论社区，和更少的影响它的因素，我们提出了一个生成模型的欺骗，结合欺骗分类，我们用来探索欺骗的流行在六个流行的网上评论社区：Expedia，Hotels.com，Orbitz，Priceline，TripAdvisor，和Yelp。我们还提出了一个基于经济信号理论的在线评论的理论模型，其中消费者评论通过充当产品真实未知质量的信号来减少消费者和生产者之间固有的信息不对称。我们发现，欺骗性的意见垃圾邮件是一个日益严重的问题，但不同的社区增长率。我们认为，这些比率是由每个评论社区与欺骗相关的不同信号成本驱动的，例如，发布要求。当采取措施增加信令成本时，例如，过滤由第一次评论者撰写的评论，有效地降低了欺骗流行率。
英文摘要：
Consumers' purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting "deceptive opinion spam" -- fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. But while this practice has received considerable public attention and concern, relatively little is known about the actual prevalence, or rate, of deception in online review communities, and less still about the factors that influence it. We propose a generative model of deception which, in conjunction with a deception classifier, we use to explore the prevalence of deception in six popular online review communities: Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor, and Yelp. We additionally propose a theoretical model of online reviews based on economic signaling theory, in which consumer reviews diminish the inherent information asymmetry between consumers and producers, by acting as a signal to a product's true, unknown quality. We find that deceptive opinion spam is a growing problem overall, but with different growth rates across communities. These rates, we argue, are driven by the different signaling costs associated with deception for each review community, e.g., posting requirements. When measures are taken to increase signaling cost, e.g., filtering reviews written by first-time reviewers, deception prevalence is effectively reduced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
