Artificial intelligence

Abstract: The task in this paper is to re-evaluate Burgoon’s (2012) model and a proposed linguistic model to lie detecting (see El-Zawawy, 2017) and to test their validity with the aid of one well-documented machine learning method, namely Support Vector Machine (henceforth SVM). The two corpora chosen are false statements by Joseph Biden and Donald Trump, all delivered spontaneously, to see how the proposed model as aided by SVM can detect their falsehood. Results show that the Burgoon model demonstrates stable and reliable performance, achieving high accuracy, precision, and recall, particularly in holistic analysis, where it records 91.84% accuracy and an F1-score of 90.91%. In contrast, the Proposed Model shows extreme inconsistencies, with an overall accuracy of 0.00%, suggesting that its predictions are dubious.

Keywords: SVM; Burgoon (2012); Lie detection; Deception

References:

1. Abouelenien, M., Pérez-Rosas, V., Mihalcea, R., & Burzo, M. (2015). Multimodal deception detection: Integrating speech, facial expressions, and physiological signals. Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, 589–596. https://doi.org/10.1145/2818346.2830594
2. Abouelenien, M., Pérez-Rosas, V., Mihalcea, R., Burzo, M. (2016). Deception detection using a multimodal approach. IEEE Transactions on Information Forensics and Security, 12(5), 1042–1055. https://doi.org/10.1109/TIFS.2016.2639337
3. Anolli, L., & Ciceri, R. (1997). The voice of deception: Vocal strategies of naive and able liars. Journal of Nonverbal Behavior, 21(4), 259–284. https://doi.org/10.1023/A:1024943011850
4. Arciuli, J., Mallard, D., & Villar, G. (2009). “Um, I can tell you’re lying”: Linguistic markers of deception versus truth-telling in speech. Applied Psycholinguistics, 31(3), 397–411. https://doi.org/10.1017/S014271640999017X
5. Benus, S., Enos, F., Hirschberg, J., & Shriberg, E. (2006). Pauses in deceptive speech. Proceedings of the 3rd International Conference on Speech Prosody, 591–594.
6. Burgoon, J. K., Blair, J. P., Qin, T., & Nunamaker, J. F. (2012). Detecting deception through linguistic analysis. Advances in Digital Forensics VIII, 91–101.
7. Chen, J., Wen, Z., Xu, B., & Wang, H. (2015). A voice-based deception detection system using support vector machines. Applied Acoustics, 89, 99–107. https://doi.org/10.1016/j.apacoust.2014.09.005
8. Demenko, G. (2008). Voice stress analysis for deception detection. International Journal of Speech Technology, 11(1), 63–72. https://doi.org/10.1007/s10772-008-9031-9
9. DePaulo, B. M., Lindsay, J. J., Malone, B. E., Muhlenbruck, L., Charlton, K., & Cooper, H. (2003). Cues to deception. Psychological Bulletin, 129(1), 74–118. https://doi.org/10.1037/0033-2909.129.1.74
10. Davatzikos, C., Ruparel, K., Fan, Y., Shen, D. G., Acharyya, M., Loughead, J. W., ... & Langleben, D. D. (2005). Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. Neuroimage, 28(3), 663–668. https://doi.org/10.1016/j.neuroimage.2005.06.035
11. El-Zawawy, Amr M. (2017) Towards a New Linguistic Model for Detecting Political Lies. Russian Journal of Linguistics, 21 (1), 183—202.
12. Fuller, C. M., Biros, D. P., & Wilson, R. L. (2011). Decision support for deception detection: Data and text mining techniques for automated lie detection. Decision Support Systems, 50(3), 602–614. https://doi.org/10.1016/j.dss.2010.08.012
13. Hancock, J. T., Curry, L. E., Goorha, S., & Woodworth, M. (2008). On lying and being lied to: A linguistic analysis of deception in computer-mediated communication. Discourse Processes, 45(1), 1–23. https://doi.org/10.1080/01638530701739181
14. Hirschberg, J., Benus, S., Brenier, J., Enos, F., Friedman, S., & Shriberg, E. (2012). Deceptive speech: Acoustic, prosodic, and perceptual correlates. Proceedings of Interspeech 2012, 1049–1052.
15. Kirchhübel, C., & Howard, D. M. (2011). Detecting deception from speech: A comparative study of three different approaches. Speech Communication, 53(9-10), 1076–1089.
16. Kim, J., Valstar, M. F., & Pantic, M. (2012). Fusion of facial expressions and EEG for implicit affective tagging. IEEE Transactions on Affective Computing, 3(2), 195–208. https://doi.org/10.1109/T-AFFC.2012.17
17. Kozel, F. A., Johnson, K. A., Grenesko, E. L., Laken, S. J., & George, M. S. (2009). Detecting deception using functional magnetic resonance imaging. Biological Psychiatry, 68(8), 679–686. https://doi.org/10.1016/j.biopsych.2009.05.034
18. Langleben, D. D., Loughead, J. W., Bilker, W. B., Ruparel, K., Childress, A. R., Busch, S. I., & Gur, R. C. (2005). Telling truth from lie in individual subjects with fast event-related fMRI. Human Brain Mapping, 26(4), 262–272. https://doi.org/10.1002/hbm.20191
19. Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29(5), 665–675. https://doi.org/10.1177/0146167203029005010
20. Pfister, T., Li, X., Zhao, G., & Pietikäinen, M. (2011). Differentiating spontaneous from posed facial expressions within a generic facial expression recognition framework. IEEE International Conference on Computer Vision (ICCV), 868–875.
21. Picornell, I. (2012). The linguistic markers of deception in written witness statements. International Journal of Speech, Language & the Law, 19(1), 1–23.
22. Rajoub, B. A., & Zwiggelaar, R. (2014). Thermal facial analysis for deception detection. IEEE Transactions on Information Forensics and Security, 9(6), 1015–1023. https://doi.org/10.1109/TIFS.2014.2322253
23. Reynolds, K., & Randle-Short, J. (2011). Linguistic analysis of deception in naturalistic settings. Language and Communication, 31(1), 51–64.
24. Vrij, A., Mann, S., Leal, S., & Fisher, R. P. (2008). Detecting deception by manipulating cognitive load. Trends in Cognitive Sciences, 12(4), 141–147.
25. Vrij, A., Mann, S., & Fisher, R. P. (2000). An empirical test of the Behaviour Analysis Interview. Law and Human Behavior, 24(3), 313–329.
26. Wang, Y., Roesch, E. B., & Pantic, M. (2013). Automatic detection of deception in videos. IEEE Transactions on Affective Computing, 5(1), 58–68. https://doi.org/10.1109/T-AFFC.2013.9
27. Zhang, X., Wang, Y., Zhang, B., & Ji, Q. (2013). A multimodal approach for deception detection. IEEE Transactions on Cybernetics, 45(3), 492–506. https://doi.org/10.1109/TCYB.2013.2293433
28. Zhou, L., Burgoon, J. K., & Twitchell, D. P. (2004a). A longitudinal analysis of language behavior in deception. Proceedings of the Hawaii International Conference on System Sciences, 1–10.
29. Zuckerman, M., DePaulo, B. M., & Rosenthal, R. (1981). Verbal and nonverbal communication of deception. Advances in Experimental Social Psychology, 14, 1–59. https://doi.org/10.1016/S0065-2601(08)60369-X