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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Fuller, C., Biros, D.P., Adkins, M., Burgoon, J.K., Nunamaker, J.F., Coulon, S. (2006). Detecting Deception in Person-of-Interest Statements. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_48
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DOI: https://doi.org/10.1007/11760146_48
Publisher Name: Springer, Berlin, Heidelberg
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