Contribution to Book
Detecting Deception in Person-of-Interest Statements
Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006. Proceedings
(2006)
Abstract
Most humans cannot detect lies at a rate better than chance. Alternative methods of deception detection may increase accuracy, but are intrusive, do not offer immediate feedback, or may not be useful in all situations. Automated classification methods have been suggested as an alternative to address these issues, but few studies have tested their utility with real-world, high-stakes statements. The current paper reports preliminary results from classification of actual security police investigations collected under high stakes and proposes stages for conducting future analyses.
Keywords
- data encryption,
- information systems applications,
- information storage and retrieval,
- computer communication networks,
- computers and society,
- legal aspects of computing
Disciplines
Publication Date
2006
Editor
Sharad Mehrotra, Daniel D. Zeng, Hsinchun Chen, Bhavani Thuraisingham, and Fei-Yue Wang
Publisher
Springer Berlin Heidelberg
Series
Lecture Notes in Computer Science
ISBN
9783540344780
DOI
10.1007/11760146_48
Citation Information
Christie Fuller, David P. Biros, Mark Adkins, Judee K. Burgoon, et al.. "Detecting Deception in Person-of-Interest Statements" Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006. Proceedings Vol. 3975 (2006) p. 504 - 509 Available at: http://works.bepress.com/christie-fuller/5/