An Exploratory Study into Deception Detection in Text-Based Computer-Mediated Communication
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track1 - Volume 1
The Impact of Media Richness, Suspicion, and Perceived Truth Bias on Deception Detection
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
Beyond keyword filtering for message and conversation detection
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Making sense of archived e-mail: Exploring the Enron collection with NetLens
Journal of the American Society for Information Science and Technology
"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
Deception detection for the tangled web
ACM SIGCAS Computers and Society
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Deception theory suggests that deceptive writing is characterized by reduced frequency of first-person pronouns and exclusive words, and elevated frequency of negative emotion words and action verbs. We apply this model of deception to the Enron email dataset, and then apply singular value decomposition to elicit the correlation structure between emails. Those emails that have high scores using this approach include deceptive emails; other emails that score highly using these frequency counts also indicate organizational dysfunctions such as improper communication of information. Hence this approach can be used as a tool for both external investigation of an organization, and internal management and regulatory compliance.