Deception and design: the impact of communication technology on lying behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Do online reviews affect product sales? The role of reviewer characteristics and temporal effects
Information Technology and Management
The lie detector: explorations in the automatic recognition of deceptive language
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
NordSec'12 Proceedings of the 17th Nordic conference on Secure IT Systems
A tentative study on the annotation of evidentiality
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Spotting opinion spammers using behavioral footprints
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Instant foodie: predicting expert ratings from grassroots
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.