Estimating the prevalence of deception in online review communities

By Myle Ott, Claire Cardie, Jeffrey T. Hancock. The Web Conference, 2012

324 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a2c07dd9fcc28e7e10250bee860bdf904d2ce215

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Citations in the corpus

What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “Therefore, the fundamental research question that this study endeavors to address is: Can nonverbal behaviors of reviewers serve as effective cues/features for fake review detection, and if so, to what extent? Although many verbal features have been used in online fake review detection models and pr”
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (2015, background): “At a high level, those can be categorized as linguistic approaches [6, 21, 22] that analyze the language patterns of spam vs.”
How Community Feedback Shapes User Behavior (2014, background): “…a growing concern (Heymann, Koutrika, and Garcia-Molina 2007), as emphasized by work on review spamming (Lim et al. 2010; Mukherjee, Liu, and Glance 2012; Ott, Cardie, and Hancock 2012), trolling (Shachaf and Hara 2010), social deviance (Shores et al. 2014) and online harassment (Yin et al. 2009).”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “, genre or authorship detection tries to determine the genre or the person who has written a text/opinion [22,78,56,79] or opinion spam detection tries to detect opinions or reviews which contain untrusted contents published to distort public opinion towards people, companies or products [34,65,60,9”
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud (2016, unknown): “Our findings here are higher than some previous estimates such as Ott et al. (2012). This difference is likely in part due to construction of the training data set used by Ott et al. (2012) (a limitation the authors acknowledge); the fact that Ott et al. (2012) rely only on text features to identify”
Advances in natural language processing (2015, background): “org/flutrends/) from symptoms mentioned in tweets or food-related illnesses (52), recognize deception in fake reviews (53), and identify social networks of people who interact”
Towards a General Rule for Identifying Deceptive Opinion Spam (2014, methodology): “Ott et al. created a gold-standard collection by employing Turkers to write fake reviews, and follow-up research was based on their data (Ott et al., 2012; Ott et al., 2013; Li et al., 2013b; Feng and Hirst, 2013).”
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection (2018, background): “[58] find that deceptive reviews are a growing problem on multiple platforms such as TripAdvisor and Yelp.”
Spotting opinion spammers using behavioral footprints (2013, background): “Although the actual percentage of spammers is unknown, deception prevalence studies [33, 43] have reported 815% spam rate in online review sites.”
Graph based anomaly detection and description: a survey (2014, background): “2007), opinion deception and reviews spam (Ott et al. 2012), auction fraud (Pandit et al.”