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Detecting Deception in Person-of-Interest Statements

  • Christie Fuller
  • David P. Biros
  • Mark Adkins
  • Judee K. Burgoon
  • Jay F. NunamakerJr.
  • Steven Coulon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)

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

Nonverbal Behavior Security Force Deception Detection Practical Machine Learn Tool Deceptive Statement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christie Fuller
    • 1
  • David P. Biros
    • 1
  • Mark Adkins
    • 1
  • Judee K. Burgoon
    • 1
  • Jay F. NunamakerJr.
    • 1
  • Steven Coulon
    • 1
  1. 1.Spears School of BusinessOklahoma State UniversityStillwaterUSA

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