Automatic detection of deceit in verbal communication

Authors:
Rada Mihalcea;Verónica Pérez-Rosas;Mihai Burzo
Affiliations:
University of Michigan, Ann Arbor, MI, USA;University of North Texas, Denton, TX, USA;University of Michigan, Flint, MI, USA
Venue:
Proceedings of the 15th ACM on International conference on multimodal interaction
Year:
2013

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Abstract

This paper presents experiments in building a classifier for the automatic detection of deceit. Using a dataset of deceptive videos, we run several comparative evaluations focusing on the verbal component of these videos, with the goal of understanding the difference in deceit detection when using manual versus automatic transcriptions, as well as the difference between spoken and written lies. We show that using only the linguistic component of the deceptive videos, we can detect deception with accuracies in the range of 52-73%.