Discriminating deception from truth and misinformation: an intent-level approach

被引:3
作者
Li, Deqing [1 ]
Santos, Eugene, Jr. [1 ]
机构
[1] Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA
关键词
Deception detection; misinformation; reasoning; Bayesian network; user modelling; DETECTING DECEPTION; TRUST; INFORMATION; FRAMEWORK; LIES; CUES;
D O I
10.1080/0952813X.2019.1652354
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:373 / 407
页数:35
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