Gender Differences in Multimodal Contact-Free Deception Detection

In this paper, we explore the hypothesis that multimodal features as well as demographic information can play an important role in increasing the performance of automatic lie detection. We introduce a large, multimodal deception detection dataset balanced across genders, and we analyze the patterns associated with the thermal, linguistic, and visual responses of liars and truth-tellers. We show that our multimodal noncontact deception detection approach can lead to a performance in the range of 60%–80%, with different modalities, different genders, and different domain settings playing a role in the accuracy of the system.

[1]  Gwen Littlewort,et al.  Automatic Recognition of Facial Actions in Spontaneous Expressions , 2006, J. Multim..

[2]  Jay F. Nunamaker,et al.  An exploratory study on promising cues in deception detection and application of decision tree , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[3]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[4]  Xiaofei Lu,et al.  Automatic analysis of syntactic complexity in second language writing , 2010 .

[5]  Frank Rudzicz,et al.  Automatic detection of deception in child-produced speech using syntactic complexity features , 2013, ACL.

[6]  Yejin Choi,et al.  Syntactic Stylometry for Deception Detection , 2012, ACL.

[7]  Mohamed Abouelenien,et al.  Detecting Deceptive Behavior via Integration of Discriminative Features From Multiple Modalities , 2017, IEEE Transactions on Information Forensics and Security.

[8]  Costanza Navarretta,et al.  The MUMIN coding scheme for the annotation of feedback, turn management and sequencing phenomena , 2007, Lang. Resour. Evaluation.

[9]  Rosanna E. Guadagno,et al.  Dating deception: Gender, online dating, and exaggerated self-presentation , 2012, Comput. Hum. Behav..

[10]  Doron Cohen,et al.  Nonverbal indicators of deception: How iconic gestures reveal thoughts that cannot be suppressed , 2010 .

[11]  Mohamed Abouelenien,et al.  Deception detection using a multimodal approach , 2014, ICMI.

[12]  Verónica Pérez-Rosas,et al.  Experiments in Open Domain Deception Detection , 2015, EMNLP.

[13]  Hugo Liu,et al.  Of Men, Women, and Computers: Data-driven Gender Modeling for Improved User Interfaces , 2022 .

[14]  Kent Marett,et al.  Gender Differences in Deception and Its Detection Under Varying Electronic Media Conditions , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[15]  Rainer Michael Rilke,et al.  Lying and Team Incentives , 2013, Social Science Research Network.

[16]  Rada Mihalcea,et al.  Linguistic Ethnography: Identifying Dominant Word Classes in Text , 2009, CICLing.