Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

By Michael Luca, Georgios Zervas. 2016

1,143 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Safety, Robustness & Interpretability

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Citations in the corpus

Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019, background): “(National Literacy Trust,2018). Aside from news, crowdturﬁng of fake reviews on websites such as Amazon and Yelp signiﬁ- cantly undermines user trust (Wang et al.,2012; Song et al.,2015). According toLuca and Zervas (2016), approximately 16% of Yelp restaurant reviews were fraudulent as of 2012. Rec”
The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study (2016, methodology): “In one setting, we use 1600 fake reviews , based on current estimates of 16% (Luca and Zervas, 2015).”
Digital Discrimination: The Case of Airbnb.com (2014, background): “Our findings contribute to the empirical market design literature on improvements to reputation systems (Dai et al 2013, Dover et al forthcoming, Luca and Zervas 2013, Bolton et al 2013).”
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection (2019, background): “ke reviews, fake comments, or fake news can then be used to attack online systems or fool human readers. For example, a review system can be ﬂooded with positive reviews to increase a company’s proﬁt [8] or with negative reviews to reduce a competitor’s proﬁt, and fake comments/news can be posted on”
False Information on Web and Social Media: A Survey (2018, background): “With primary motives of influencing opinions and earning money [1, 46, 56, 94], the wide impact of false information makes it one of the modern dangers to society, according to the World Economic Forum [39].”
When and how Managers' Responses to Online Reviews Affect Subsequent Reviews (2018, neutral): “While the extant research has primarily focused on identifying the presence and efficacy of “promotional” reviews as an intervention strategy (Luca and Zervas 2016; Mayzlin, Dover, and Chevalier 2014) researchers have largely overlooked the ubiquitous practice of manager response (MR), defined as…”
Collective Opinion Spam Detection: Bridging Review Networks and Metadata (2015, background): “The problem is surprisingly prevalent; it is estimated that more than 20% of Yelp’s reviews are fake [3], with steady growth [16], while one-third of all consumer reviews on the GREAT place to eat at!!!!!”
Human and Automatic Detection of Generated Text (2019, background): “According to Luca and Zervas (2016), approximately 16% of Yelp restaurant reviews were fraudulent as of 2012.”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “Those fake reviews can not only mislead consumers to make incorrect decisions, but also fundamentally undermine the credibility and value of reviews as a whole [26, 37].”