Making large-scale support vector machine learning practical
Advances in kernel methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication
Journal of Management Information Systems
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
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Proceedings of the 18th international conference on World wide web
Automatically assessing the post quality in online discussions on software
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
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The lie detector: explorations in the automatic recognition of deceptive language
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Estimating Labels from Label Proportions
The Journal of Machine Learning Research
An exploration of off topic conversation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Amazon Mechanical Turk for subjectivity word sense disambiguation
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement
ACM SIGKDD Explorations Newsletter
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IEEE Transactions on Neural Networks
Detection of near-duplicate user generated contents: the SMS spam collection
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Social transparency in networked information exchange: a theoretical framework
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Identifying spam in the iOS app store
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
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Serf and turf: crowdturfing for fun and profit
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Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying
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Stylometric analysis of scientific articles
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Seeing through deception: a computational approach to deceit detection in written communication
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
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EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Simultaneously detecting fake reviews and review spammers using factor graph model
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable 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 psychology and computational linguistics, we develop and compare three approaches to detecting 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 models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.