Deception and design: the impact of communication technology on lying behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automated Linguistic Analysis of Deceptive and Truthful Synchronous Computer-Mediated Communication
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 01
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication
Journal of Management Information Systems
Improving user experience with case-based reasoning systems using text mining and Web 2.0
Expert Systems with Applications: An International Journal
Measuring firm performance using financial ratios: A decision tree approach
Expert Systems with Applications: An International Journal
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
Hi-index | 12.06 |
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.