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
Adaptive context modeling for deception detection in emails
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Hybrid feature selection for phishing email detection
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
Effects of Automated and Participative Decision Support in Computer-Aided Credibility Assessment
Journal of Management Information Systems
Journal of Data and Information Quality (JDIQ)
An experimental comparison of real and artificial deception using a deception generation model
Decision Support Systems
On the use of homogenous sets of subjects in deceptive language analysis
EACL 2012 Proceedings of the Workshop on Computational Approaches to Deception Detection
A study of manipulative and authentic negative reviews
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Online deception is disrupting our daily life, organizational process, and even national security. Existing approaches to online deception detection follow a traditional paradigm by using a set of cues as antecedents for deception detection, which may be hindered by ineffective cue identification. Motivated by the strength of statistical language models (SLMs) in capturing the dependency of words in text without explicit feature extraction, we developed SLMs to detect online deception. We also addressed the data sparsity problem in building SLMs in general and in deception detection in specific using smoothing and vocabulary pruning techniques. The developed SLMs were evaluated empirically with diverse datasets. The results showed that the proposed SLM approach to deception detection outperformed a state-of-the-art text categorization method as well as traditional feature-based methods.