Real or Spiel? A Decision Tree Approach for Automated Detection of Deceptive Language-Action Cues
As the use of computer-mediated communications has increased, the potential risk of online deception has grown — as has the importance of better understanding human behavior online to mitigate these risks. Previous research has demonstrated that linguistic features provide crucial cues to detect deception, and that reasonable accuracy in detection of deception can be achieved by applying certain classification methodologies to these cues. This paper expands on this line of inquiry, and presents findings from a study conducted in the Spring of 2015. Our findings suggest a viable process for and the feasibility of using a decision-tree classification approach to develop an automated process to detect deception in computer-mediated communications.