Detecting deception in secondary screening interviews using linguistic analysis

Abstract

Ensuring security in transportation is a challenging problem. Many technologies have been implemented for primary screening, but less has been done to improve the secondary screening process. This paper introduces two methods that may aid in detecting deception during the interviews characteristic of secondary screening. First, message feature mining uses message features or cues combined with machine learning techniques to classify messages according to their deceptive potential. Second, speech act profiling, a method for quantifying and visualizing entire conversations, has shown promise in aiding deception detection. These methods may be combined and are intended to be a part of a suite of tools for automating deception detection.