Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
MailRank: using ranking for spam detection
Proceedings of the 14th ACM international conference on Information and knowledge management
A reference collection for web spam
ACM SIGIR Forum
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A Statistical Language Modeling Approach to Online Deception Detection
IEEE Transactions on Knowledge and Data Engineering
Do online reviews matter? - An empirical investigation of panel data
Decision Support Systems
'Helpfulness' in online communities: a measure of message quality
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
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
IEEE Transactions on Knowledge and Data Engineering
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
Decision Support Systems
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Electronic Commerce Research and Applications
"I don't know where he is not": does deception research yet offer a basis for deception detectives?
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
In search of a gold standard in studies of deception
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
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Given users' growing penchant to use online reviews for travel planning, the business malpractice of posting manipulative reviews to distort the reputation of hotels is on the rise. Some manipulative reviews could be positive and intended to boost own offerings, while others could be negative and meant to slander competing ones. However, most scholarly inquiry hitherto has been trained on the former. Hence, this paper investigates the extent to which linguistic cues such as readability, genre and writing style of negative reviews could help predict if they are manipulative or authentic. Analysis of a publicly available dataset of 800 negative reviews (400 manipulative + 400 authentic) indicates that manipulative reviews are generally less readable than authentic reviews. In terms of genre, although manipulative reviews should be imaginative and authentic reviews informative, spammers appear adept enough to blur the line between the two. With respect to writing style, manipulative reviews are more richly embellished with affective cues and perceptual words.