Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 309–319, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Finding Deceptive Opinion Spam by Any Stretch of the Imagination Myle Ott Yejin Choi Claire Cardie Department of Computer Science Cornell University Ithaca, NY 14853 {myleott,ychoi,cardie}@cs.cornell.edu Jeffrey T. Hancock Department of Communication Cornell University Ithaca, NY 14853 jth34@cornell.edu Abstract Consumers increasingly rate, review and re- search products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites con- taining consumer reviews are becoming tar- gets of opinion spam. While recent work has focused primarily on manually identifi- able instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. Integrating work from psy- chology and computational linguistics, we de- velop and compare three approaches to detect- ing deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned mod- els, we additionally make several theoretical contributions, including revealing a relation- ship between deceptive opinions and imagina- tive writing. 1 Introduction With the ever-increasing popularity of review web- sites that feature user-generated opinions (e.g., TripAdvisor1 and Yelp2), there comes an increasing potential for monetary gain through opinion spam— inappropriate or fraudulent reviews. Opinion spam can range from annoying self-promotion of an un- related website or blog to deliberate review fraud, as in the recent case3 of a Belkin employee who 1http://tripadvisor.com 2http://yelp.com 3http://news.cnet.com/8301-1001_ 3-10145399-92.html hired people to write positive reviews for an other- wise poorly reviewed product.4 While other kinds of spam have received consid- erable computational attention, regrettably there has been little work to date (see Section 2) on opinion spam detection. Furthermore, most previous work in the area has focused on the detection of DISRUPTIVE OPINION SPAM—uncontroversial instances of spam that are easily identified by a human reader, e.g., ad- vertisements, questions, and other irrelevant or non- opinion text (Jindal and Liu, 2008). And while the presence of disruptive opinion spam is certainly a nuisance, the risk it poses to the user is minimal, since the user can always choose to ignore it. We focus here on a potentially more insidi- ous type of opinion spam: DECEPTIVE OPINION SPAM—fictitious opinions that have been deliber- ately written to sound authentic, in order to deceive the reader. For example, one of the following two hotel reviews is truthful and the other is deceptive opinion spam: 1. I have stayed at many hotels traveling for both business and pleasure and I can honestly stay that The James is tops. The service at the hotel is first class. The rooms are modern and very comfortable. The location is per- fect within walking distance to all of the great sights and restaurants. Highly recommend to both business trav- ellers and couples. 2. My husband and I stayed at the James Chicago Hotel for our anniversary. This place is fantastic! We knew as soon as we arrived …

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