Open AccessProceedings Article
Negative Deceptive Opinion Spam
Myle Ott,Claire Cardie,Jeffrey T. Hancock +2 more
- pp 497-501
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TLDR
This work creates and study the first dataset of deceptive opinion spam with negative sentiment reviews, and finds that standard n-gram text categorization techniques can detect negative deceptive opinions spam with performance far surpassing that of human judges.Abstract:
The rising influence of user-generated online reviews (Cone, 2011) has led to growing incentive for businesses to solicit and manufacture DECEPTIVE OPINION SPAM—fictitious reviews that have been deliberately written to sound authentic and deceive the reader. Recently, Ott et al. (2011) have introduced an opinion spam dataset containing gold standard deceptive positive hotel reviews. However, the complementary problem of negative deceptive opinion spam, intended to slander competitive offerings, remains largely unstudied. Following an approach similar to Ott et al. (2011), in this work we create and study the first dataset of deceptive opinion spam with negative sentiment reviews. Based on this dataset, we find that standard n-gram text categorization techniques can detect negative deceptive opinion spam with performance far surpassing that of human judges. Finally, in conjunction with the aforementioned positive review dataset, we consider the possible interactions between sentiment and deception, and present initial results that encourage further exploration of this relationship.read more
Citations
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A deceptive detection model based on topic, sentiment, and sentence structure information
TL;DR: A new model called Sentence Joint Topic Sentiment Model (SJTSM) is presented, which incorporates the sentence structure of reviews and the sentiment label information of words based on Latent Dirichlet Allocation model to extract the review features.
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Gender deception in asynchronous online communication: A path analysis
TL;DR: Cognitive factors of gender deception were analyzed, to support hypotheses that an actor’s actual gender can affect the motivation to deceive and suggest that the gender of the message recipient could be a significant factor in uncovering gender deception.
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Cooks Distance and Mahanabolis Distance Outlier Detection Methods to identify Review Spam
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Gender Deception in Asynchronous Online Communication: A Path Analysis
TL;DR: In this article, the authors used path analysis to examine interconnected cognitive factors that impact online users' ability to deceive and detect deception concerning gender in an asynchronous online game, where males were incentivized to communicate like females, and females were incentivised to behave like males.
Book ChapterDOI
Detecting deceptive intentions: Possibilities for large-scale applications
TL;DR: A set of criteria that an applied system should meet from a practitioner’s perspective to evaluate deception theories, interviewing approaches, information elicitation methods, and verbal deception cues that may be of use for large-scale applications for prospective airport passenger screening are outlined.
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