Detecting Concealed Information on a Large Scale

Kleinberg, Bennett; Toolen, Yaloe van der; Arntz, Arnoud; Verschuere, Bruno

doi:10.1016/b978-0-12-812729-2.00016-1

Cited by 5 publications

(3 citation statements)

References 63 publications

(87 reference statements)

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“…The model statement facilitated the elicitation of information in all eight deception studies published to date that we are aware of (Bogaard, Meijer, & Vrij, 2014;Ewens et al, 2016;Harvey, Vrij, Leal, Lafferty, & Nahari, 2017;Kleinberg, van der Toolen, Vrij, Arntz, & Verschuere, 2018;Leal et al, 2015;Porter et al, 2017;Vrij, Leal, Jupe, & Harvey, 2018;. However, the technique has generally been unsuccessful at enhancing the differences between truth tellers and liars regarding the amount of details they provide, as both groups tend to add a similar amount of detail when exposed to a model statement (but see Porter et al [2017] for an exception).…”

mentioning

confidence: 99%

Using the model statement to elicit verbal differences between truth tellers and liars: The benefit of examining core and peripheral details.

Leal

Vrij

Deeb

et al. 2018

Journal of Applied Research in Memory and Cognition

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Effective model statement use to elicit veracity differences in reporting details 2 Abstract Research has shown that a model statement elicits more information during an interview and that truth tellers and liars report a similar amount of extra information. We hypothesised that Veracity differences would arise if the total amount of information would be split up into core details and peripheral details. A total of 119 truth tellers and liars reported a stand-out event that they had experienced in the last two years. Truth tellers had actually experienced the event and liars made up a story. Half of the participants were given a model statement during the interview. After exposure to a model statement, truth tellers and liars reported a similar amount of extra core information, but liars reported significantly more peripheral information. The variable 'details' becomes an indicator of deceit in a model statement interview protocol as long as a distinction is made between core and peripheral details.Effective model statement use to elicit veracity differences in reporting details 3 General audience summaryResearch has shown that a model statement, an example of a detailed report about an event unrelated to the topic of investigation, elicits more information during an interview and that truth tellers and liars report a similar amount of extra information. Interviewees typically underestimate how much information is required from them in an interview and the model raises the expectations amongst both truth tellers and liars about how much information they should provide. We tested the hypothesis that differences between truth tellers and liars would arise if the total amount of information was split into two categories: core details and peripheral details. A total of 119 truth tellers and liars reported a stand-out event that they had experienced in the last two years. Truth tellers had actually experienced the event and liars made up a story. Half of the participants were given a model statement during the interview.After exposure to a model statement, truth tellers and liars reported a similar amount of extra core information, but liars reported significantly more peripheral information. The variable 'details' becomes an indicator of deceit in a model statement interview protocol as long as a distinction is made between core and peripheral details.Effective model statement use to elicit veracity differences in reporting details 4 Using the Model Statement to Elicit Verbal Differences between Truth tellers and Liars:

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confidence: 99%

Using the model statement to elicit verbal differences between truth tellers and liars: The benefit of examining core and peripheral details.

Leal

Vrij

Deeb

et al. 2018

Journal of Applied Research in Memory and Cognition

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“…Interestingly, even small to moderate average effects of automated coding could be of interest for the study of deception. Small to moderate effects of automated coding may even be of use for lie detection purposes when combined with other cues (e.g., Fitzpatrick, Bachenko, & Fornaciari, 2015;Kleinberg, van der Toolen, Vrij, Arntz, & Verschuere, 2018;Ott, Choi, Cardie, & Hancock, 2011). Furthermore, computer coding can be fully automated.…”

Section: Discussionmentioning

confidence: 99%

Man versus Machine: Comparing manual with LIWC coding of perceptual and contextual details for verbal lie detection

Schütte¹,

Bogaard²,

Giolla³

et al. 2021

Preprint

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Purpose: Truthful statements are theorized to be richer in perceptual and contextual detail than deceptive statements. The level of detail can be coded by humans or computers, with human coding argued to be superior. Direct comparisons of human and automated coding, however, are rare.Methods: We applied automatic identification of details with the Linguistic Inquiry and Word Count (LIWC) software on truthful and deceptive statements from four datasets that had been manually coded for details.Results: We noted that the common way of scoring manual and LIWC coding hampers a direct comparison because they rely on different metrics – count and proportion scores, respectively. Lie-truth differences varied substantially across metric and dataset (LIWC: -0.09 ≤ Cohen’s d ≤ .89; Manual: 0.03 ≤ Cohen’s d ≤ .80). When set to the same metric, neither method seemed to outperform the other. Using count scores, both LIWC and manual coding indicated that truthful statements about past events contain more perceptual and contextual details than deceptive statements. Across the four datasets, we also observed considerable variation in manual coding.Conclusions: Human coding does not necessarily outperform LIWC coding of perceptual and contextual details in discriminating lies from truths. Our findings call for systematic comparison of human and automated verbal lie detection approaches on the same data, and we reiterate the need for better data sharing practices to help accomplish that aim.

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“…This means that of all the documents classified as deriving from violence-actualisers (total 1,009,800), less than 1% are correctly classified (0.98% or 9,900 out of 1,009,800) as being truly written by a violenceactualiser. See Table 1 for a breakdown of this calculation (adapted from Kleinberg et al, 2018;van der Vegt et al, 2019). It is important to recognise that even if prediction systems could be further developed to achieve high accuracy rates in violence prediction, the base rate fallacy will continue to persist.…”

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confidence: 99%

Proceed with Caution: On the Use of Computational Linguistics in Threat Assessment

Vegt¹,

Kleinberg²,

Gill³

2022

Preprint

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Large-scale linguistic analyses are increasingly applied to the study of extremism, terrorism, and other threats of violence. At the same time, practitioners working in the field of counterterrorism and security are confronted with large-scale linguistic data, and may benefit from computational methods. This article highlights the challenges and opportunities associated with applying computational linguistics in the domain of threat assessment. Four current issues are identified, namely 1) the data problem, 2) the utopia of predicting violence, 3) the base rate fallacy, and 4) the danger of closed-sourced tools. These challenges are translated into a checklist of questions that should be asked by policymakers and practitioners who (intend to) make use tools that leverage computational linguistics for threat assessment. The ‘VISOR-P’ checklist can be used to evaluate such tools through their Validity, Indicators, Scientific Quality, Openness, Relevance and Performance. Finally, some suggestions are outlined for the furtherance of the computational linguistic threat assessment field.

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Detecting Concealed Information on a Large Scale

Abstract: Link to publication License Article 25fa Dutch Copyright Act

Cited by 5 publications

References 63 publications

Using the model statement to elicit verbal differences between truth tellers and liars: The benefit of examining core and peripheral details.

Using the model statement to elicit verbal differences between truth tellers and liars: The benefit of examining core and peripheral details.

Man versus Machine: Comparing manual with LIWC coding of perceptual and contextual details for verbal lie detection

Proceed with Caution: On the Use of Computational Linguistics in Threat Assessment

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