An investigation of data and text mining methods for real world deception detection

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.

Research highlights
► In this research study, data and text mining methods are used to detect deception.
► The sample was real-world data from a high-stakes situation.
► 13 variables were selected as model inputs using Chi-square feature selection.
► Information fusion based classification models produced better than 74% classification accuracy.
► Neural networks and decision trees were 73.46% and 71.60% accurate, respectively.