Estimating the Prevalence of Deception in Online Review Communities

TL;DR

This paper investigates the prevalence of deceptive reviews in online communities using a generative model and classifier, revealing that deception is increasing and can be mitigated by increasing signaling costs.

Contribution

It introduces a generative deception model and a theoretical signaling framework to quantify and analyze deception rates across multiple review platforms.

Findings

Deceptive opinion spam is a growing problem with varying rates across communities.

Increasing signaling costs, like filtering first-time reviewers, reduces deception prevalence.

Deception growth rates differ due to community-specific signaling costs.

Abstract

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.