Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 7, Pages 8392-8398Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.01.032
Keywords
Deception detection; Data mining; Text mining; Information fusion; Classification; Credibility assessment
Categories
Funding
- Defense Academy for Credibility Assessment [DoDPI06-P-0041, W991NF-07-1-0375]
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Uncovering lies (or deception) is of critical importance to many including law enforcement and security personnel. Though these people may try to use many different tactics to discover deception, previous research tells us that this cannot be accomplished successfully without aid. This manuscript reports on the promising results of a research study where data and text mining methods along with a sample of real-world data from a high-stakes situation is used to detect deception. At the end, the information fusion based classification models produced better than 74% classification accuracy on the holdout sample using a 10-fold cross validation methodology. Nonetheless, artificial neural networks and decision trees produced accuracy rates of 73.46% and 71.60% respectively. However, due to the high stakes associated with these types of decisions, the extra effort of combining the models to achieve higher accuracy is well warranted. (C) 2011 Elsevier Ltd. All rights reserved.
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