Discriminating deception from truth and misinformation: an intent-level approach. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: Li, Deqing; Santos, Eugene
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 32:Issue 3(2020)
• Page Start: 373
• Page End: 407
• Publication Date: 2020-05-03
• Subjects: Deception detection -- misinformation -- reasoning -- Bayesian network -- user modelling; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2019.1652354
• Languages: English
• ISSNs: 0952-813X

Abstract

ABSTRACT: Deception detection has been studied for hundreds of years. A particularly challenging problem is to not only identify truth from deception, but also discriminate misinformation, i.e. errors, from deception. Misinformation has generally been ignored in the study of deception detection, but through analysing the foundations of deception, it may be possible to pinpoint a fundamental difference between deception and all other benign communications – namely, the intent of the speaker. We present a detection model that captures a speaker's intent by measuring his patterns of reasoning. The reasoning patterns of deceivers may serve as indicators of intentional deception. Through empirical studies, these intent-driven reasoning patterns can identify as well as explain deceptive communications.