Literature DB >> 25387767

Are computers effective lie detectors? A meta-analysis of linguistic cues to deception.

Valerie Hauch1, Iris Blandón-Gitlin2, Jaume Masip3, Siegfried L Sporer4.   

Abstract

This meta-analysis investigates linguistic cues to deception and whether these cues can be detected with computer programs. We integrated operational definitions for 79 cues from 44 studies where software had been used to identify linguistic deception cues. These cues were allocated to six research questions. As expected, the meta-analyses demonstrated that, relative to truth-tellers, liars experienced greater cognitive load, expressed more negative emotions, distanced themselves more from events, expressed fewer sensory-perceptual words, and referred less often to cognitive processes. However, liars were not more uncertain than truth-tellers. These effects were moderated by event type, involvement, emotional valence, intensity of interaction, motivation, and other moderators. Although the overall effect size was small, theory-driven predictions for certain cues received support. These findings not only further our knowledge about the usefulness of linguistic cues to detect deception with computers in applied settings but also elucidate the relationship between language and deception.
© 2014 by the Society for Personality and Social Psychology, Inc.

Keywords:  computer program; detection of deception; linguistic cues; meta-analysis

Mesh:

Year:  2014        PMID: 25387767     DOI: 10.1177/1088868314556539

Source DB:  PubMed          Journal:  Pers Soc Psychol Rev        ISSN: 1532-7957


  11 in total

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Authors:  Todd Rogers; Leanne Ten Brinke; Dana R Carney
Journal:  Proc Natl Acad Sci U S A       Date:  2016-05-23       Impact factor: 11.205

2.  The inhibitory spillover effect: Controlling the bladder makes better liars.

Authors:  Elise Fenn; Iris Blandón-Gitlin; Jennifer Coons; Catherine Pineda; Reinalyn Echon
Journal:  Conscious Cogn       Date:  2015-09-11

3.  A reverse order interview does not aid deception detection regarding intentions.

Authors:  Elise Fenn; Mollie McGuire; Sara Langben; Iris Blandón-Gitlin
Journal:  Front Psychol       Date:  2015-08-31

4.  Strategic Interviewing to Detect Deception: Cues to Deception across Repeated Interviews.

Authors:  Jaume Masip; Iris Blandón-Gitlin; Carmen Martínez; Carmen Herrero; Izaskun Ibabe
Journal:  Front Psychol       Date:  2016-11-01

5.  Learning to Detect Deception from Evasive Answers and Inconsistencies across Repeated Interviews: A Study with Lay Respondents and Police Officers.

Authors:  Jaume Masip; Carmen Martínez; Iris Blandón-Gitlin; Nuria Sánchez; Carmen Herrero; Izaskun Ibabe
Journal:  Front Psychol       Date:  2018-01-04

6.  Deception and Cognitive Load: Expanding Our Horizon with a Working Memory Model.

Authors:  Siegfried L Sporer
Journal:  Front Psychol       Date:  2016-04-07

7.  Scientific Content Analysis (SCAN) Cannot Distinguish Between Truthful and Fabricated Accounts of a Negative Event.

Authors:  Glynis Bogaard; Ewout H Meijer; Aldert Vrij; Harald Merckelbach
Journal:  Front Psychol       Date:  2016-02-25

8.  Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling.

Authors:  Bennett Kleinberg; Yaloe van der Toolen; Aldert Vrij; Arnoud Arntz; Bruno Verschuere
Journal:  Appl Cogn Psychol       Date:  2018-04-02

9.  Separating the Wheat From the Chaff: Guidance From New Technologies for Detecting Deception in the Courtroom.

Authors:  Judee K Burgoon
Journal:  Front Psychiatry       Date:  2019-01-17       Impact factor: 4.157

10.  Sophisticated Deception in Junior Middle School Students: An ERP Study.

Authors:  Haizhou Leng; Yanrong Wang; Qian Li; Lizhu Yang; Yan Sun
Journal:  Front Psychol       Date:  2019-01-11
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