Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior
IEEE Intelligent Systems
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication
Journal of Management Information Systems
Following linguistic footprints: automatic deception detection in online communication
Communications of the ACM - Enterprise information integration: and other tools for merging data
Commercial Internet filters: Perils and opportunities
Decision Support Systems
Judging the Credibility of Information Gathered from Face-to-Face Interactions
Journal of Data and Information Quality (JDIQ)
"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
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Automatic deception detection (ADD) becomes more and more important. ADD can be facilitated with the development of data mining techniques. In the paper we focus on decision tree to automatic classify deceptions. The major question is how to select experiment data (input data for training in decision tree) so that it maximally benefits the decision tree performance. We investigate promising level of the cues of experiment data, and then adjust the applications in decision tree accordingly. Five comparative decision tree experiments demonstrate that tree performance, such as accurate rate and complexity, is dramatically improved by statistically and semantically selecting cues.