Research Highlights
Online and digital communication, such as emails, instant messaging, online chat, and discussion forums, become increasingly popular. Accordingly, online deception and fraud also rise, leading to significant financial loss to individuals and organizations. Decades of research on deception detection has shown that detection of deception is very challenging due to truth bias. Because the commonly used cues to deception in traditional face-to-face communication, such as facial expression, body gesture, and physiological feature changes, are often not available in online communication, making deception detection in the latter even more challenging.
Our research in this area focuses on achieving a better understanding of deceptive behavior and strategies of online deceivers, identifying effective verbal and non-verbal cues to online deception in different communication contexts (e.g., different communication media, different levels of group member familiarity and group size), and developing robust models for automated online deception detection models.
Selected Publications
Zhou, L., J. Tao, D. Zhang (2022). Do Fake News in Different Languages Tell the Same Story? An Analysis of Multi-level Thematic and Emotional Characteristics of News about COVID-19, Information Systems Frontier, September, 1-20. doi: 10.1007/s10796-022-10329-7
Shan, G., L. Zhou, and Zhang, D. (2021). From Conflicts and Confusion to Doubts: Examining Review Inconsistency for Fake Review Detection. Decision Support Systems. 144, May.
Zhou, L. J. Lim and D. Zhang, (2021). Verbal Deception Cue Training for the Detection of Phishing Emails, IEEE International Conference on Intelligence and Security Informatics, San Antonio, TX, Nov. 2-3, 2021. (Virtual)
Tao, J., X. Fang, and L. Zhou (2021), Unsupervised Deep Learning for Fake Content Detection in Social Media, Hawaii International Conference on System Sciences (HICSS-54). January 5-8, Kauai, HI, USA. (Virtual)
Zhang, D., Zhou, L., Kehoe, L. J., and Kilic, I. (2016). What Online Reviewer Behaviors Really Matter? A Study of Effects of Verbal and Nonverbal Behaviors on Online Fake Review Detection. Journal of Management Information Systems. 33(2). p.456-481 READ
Zhou, L., Wu, J., &Zhang, D. (2014). Discourse Features of Deception Behavior in the Case of Multiple Receivers. Information & Management. 51(6): 726-737 READ
Zhou, L., Song, Y., and Zhang, D. (2013). Deception performance in online group negotiation and decision making: the effects of deception experience and deception skill. Group Decision & Negotiation. 22(1), p.153-172 READ
Zhou, L. and Zhang, D. (2012). Automatic Deception Detection in Computer-Mediated Communication. IEEE Intelligent Systems. Nov./Dec., p. 73-75.
Zhou, L., Shi, Y, and Zhang, D. (2008). A Statistical Language Modeling Approach to Online Deception Detection. IEEE Transactions on Knowledge and Data Engineering (TKDE). 20(8): pp.1077-1081. READ