Are you for real? Exploring language use and unexpected process questions within the detection of identity deception

Jupe, Louise; Vrij, Aldert; Leal, Sharon; Nahari, Galit

doi:10.1002/acp.3446

Cited by 12 publications

(4 citation statements)

References 89 publications

(122 reference statements)

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“…To date, over 150 unique cues to deceit have been examined in the deception literature (DePaulo et al 2003). This includes specific behaviors, such as foot and leg movements (deTurck & Miller, 1985); more holistic person judgements, such as pleasantness and friendliness (Burgoon et al, 1996); verbal cues, such a statement detail (Köhnken et al, 1995) and consistency (Granhag & Strömwall, 1999); para-verbal cues, such as pitch (Motley, 1974) and speech rate (Riggio & Friedman, 1983); as well as linguistic cues, such as pronoun use (Newman et al, 2003) and language positivity (Jupe et al, 2018). Indeed, virtually any observable behavior can be examined as a potential cue to deception.…”

Section: Empirical Research On Cues To Deceptionmentioning

confidence: 99%

What Have We Learned About Cues to Deception? A Survey of Expert Opinions

Luke¹,

Giolla²,

Memon³

et al. 2023

Preprint

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Researchers have accumulated a substantial body of empirical work studying observable behaviors that might distinguish truth tellers from liars—that is, cues to deception. We report a survey of N = 50 deception cue experts—active researchers on deception—who provided their opinions on three issues: (1) What cues distinguish between truthful and deceptive statements? (2) What moderators influence the magnitude and direction of cues to deception? (3) What theoretical perspectives on deception cues are best supported by research? The experts displayed agreement on few issues. Expert opinion on cues to deception, potential moderators, and theoretical perspectives is mixed and often conflicting. The single issue on which more than 80% of experts agreed was that gaze aversion is not generally diagnostic of deception. This lack of consensus suggests that substantial work remains to be done before broad agreement can be established. It follows that any practical recommendation advocating the use of a specific deception cue cannot be widely representative of expert opinion.

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Section: Empirical Research On Cues To Deceptionmentioning

confidence: 99%

What Have We Learned About Cues to Deception? A Survey of Expert Opinions

Luke¹,

Giolla²,

Memon³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers have recently employed techniques such as the unexpected-question method to detect identity deception. Jupe et al 70 investigated whether contextual and perceptual details and language use could effectively differentiate identity deceivers from truth tellers in response to outcome questions (related to the outcome of an event) and unexpected process questions (specific to the planning phase or the progression experience of an event). Contrary to expectations, the findings suggested that contextual and perceptual details may not have diagnostic value when applied to the cross‐situational domain of identity deception.…”

Section: Introductionmentioning

confidence: 99%

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

Melis,

Ursino,

Scarpazza

et al. 2024

Sci Rep

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In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals’ performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

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“…These commentaries covered the essential subjects regarding human judgements of deception and highlighted paramount issues on which researchers should focus. However, computational linguistic deception detection is barely mentioned in the publication of this 3-day workshop, while the interest for these tools and methods increases (e.g., Jupe et al, 2018;Kleinberg et al, 2018;Tomas et al, 2021c).…”

Section: Introductionmentioning

confidence: 99%

Computational Measures of Deceptive Language: Prospects and Issues

2022

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In this article, we wish to foster a dialogue between theory-based and classification-oriented stylometric approaches regarding deception detection. To do so, we review how cue-based and model-based stylometric systems are used to detect deceit. Baseline methods, common cues, recent methods, and field studies are presented. After reviewing how computational stylometric tools have been used for deception detection purposes, we show that the he stylometric methods and tools cannot be applied to deception detection problems on the field in their current state. We then identify important advantages and issues of stylometric tools. Advantages encompass quickness of extraction and robustness, allowing for best interviewing practices. Issues are discussed in terms of oral data transcription issues and automation bias emergence. We finally establish future research proposals: We emphasize the importance of baseline assessment and the need for transcription methods, and the concern of ethical standards regarding the applicability of stylometry for deception detection purposes in practical settings, while encouraging the cooperation between linguists, psychologists, engineers, and practitioners requiring deception detection methods.

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Are you for real? Exploring language use and unexpected process questions within the detection of identity deception

Cited by 12 publications

References 89 publications

What Have We Learned About Cues to Deception? A Survey of Expert Opinions

What Have We Learned About Cues to Deception? A Survey of Expert Opinions

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions

Computational Measures of Deceptive Language: Prospects and Issues

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