Detecting Deception in Person-of-Interest Statements

Christie Fuller; David P. Biros; Mark Adkins; Judee K. Burgoon; Jay F. Nunamaker, Jr.; Steven Coulon

doi:10.1007/11760146_48

Detecting Deception in Person-of-Interest Statements

Christie Fuller, David P. Biros, Mark Adkins, Judee K. Burgoon, Jay F. Nunamaker, Jr., Steven Coulon

Oklahoma State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter

4 Scopus citations

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.

Original language	American English
Title of host publication	Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006. Proceedings
DOIs	https://doi.org/10.1007/11760146_48
State	Published - 2006
Externally published	Yes

Keywords

computer communication networks
computers and society
data encryption
information storage and retrieval
information systems applications
legal aspects of computing

EGS Disciplines

Business Administration, Management, and Operations
Computer Sciences

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10.1007/11760146_48License: Unspecified

Cite this

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title = "Detecting Deception in Person-of-Interest Statements",

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 = "computer communication networks, computers and society, data encryption, information storage and retrieval, information systems applications, legal aspects of computing",

author = "Christie Fuller and Biros, {David P.} and Mark Adkins and Burgoon, {Judee K.} and {Nunamaker, Jr.}, {Jay F.} and Steven Coulon",

note = "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.",

year = "2006",

doi = "10.1007/11760146_48",

language = "American English",

booktitle = "Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006. Proceedings",

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TY - CHAP

T1 - Detecting Deception in Person-of-Interest Statements

AU - Fuller, Christie

AU - Biros, David P.

AU - Adkins, Mark

AU - Burgoon, Judee K.

AU - Nunamaker, Jr., Jay F.

AU - Coulon, Steven

N1 - 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.

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - computer communication networks

KW - computers and society

KW - data encryption

KW - information storage and retrieval

KW - information systems applications

KW - legal aspects of computing

UR - http://dx.doi.org/10.1007/11760146_48

U2 - 10.1007/11760146_48

DO - 10.1007/11760146_48

M3 - Chapter

BT - Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, USA, May 23-24, 2006. Proceedings

ER -

Detecting Deception in Person-of-Interest Statements

Abstract

Keywords

EGS Disciplines

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