Automated deception detection of 911 call transcripts
This study is a successful proof of concept of using automated text analysis to accurately classify transcribed 911 homicide calls according to their veracity. Fifty matched, caller-side transcripts were labeled as truthful or deceptive based on the subsequent adjudication of the cases. We mined the transcripts and analyzed a set of linguistic features supported by deception theories. Our results suggest that truthful callers display more negative emotion and anxiety and provide more details for emergency workers to respond to the call. On the other hand, deceivers attempt to suppress verbal responses by using more negation and assent words. Using these features as input variables, we trained and tested several machine-learning classification algorithms and compared the results with the output from a statistical classification technique, discriminant analysis. The overall performance of the classification techniques was as high as 84% for the cross-validated set. The promising results of this study illustrate the potential of using automated linguistic analyses in crime investigations.
deception detection 911 calls automated linguistic analysis