When Deceitful Chats Look Truthful. Issue 4 (1st October 2016)

• Record Type: Journal Article
• Authors: McDonald, Daniel; Boyle, Randall; Anderson, John
• Is Part Of: Journal of computer information systems. Volume 56:Issue 4(2016)
• Page Start: 331
• Page End: 340
• Publication Date: 2016-10-01
• Subjects: Deception detection -- content analysis -- clustering; Electronic data processing -- Study and teaching -- Periodicals; 658.4038011
• DOI: 10.1080/08874417.2016.1164008
• Languages: English
• ISSNs: 0887-4417

Abstract

ABSTRACT: The amount of deception taking place via electronic text-based communication is increasing. Research has sought to automatically detect deception by analyzing the text from the communicator. However, the deceptive intent of the communication partner is being ignored. We compare the text from subjects who are trying to deceive each other, subjects trying to deceive truth tellers, subjects telling the truth to truth tellers, and subjects telling the truth to deceivers. We hypothesize that despite the intent of the partner, deceitful text will cluster closest to deceitful text. We cluster each of the four conditions using the text content. The cluster algorithm placed subjects trying to deceive each other closest to subjects telling the truth to each other. In this analysis, the language that led subjects to choose the same outcomes had a stronger effect than the language tied to being deceitful or truthful.