How people really detect lies

Park, Hee Sun; Levine, Timothy R.; McCornack, Steven A.; Morrison, Kelly; Ferrara, Merissa

doi:10.1080/714041710

Cited by 166 publications

(143 citation statements)

References 28 publications

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“…The relative unimportance of linguistic cues (16 percent) is evident from our proposed cue typology. Contrary to the majority of current computational efforts at the lexico-semantic level (e.g., LWICbased), this finding resonates with concerns previously expressed in psychology in regards to how people detect deception in real life (Park, et al, 2002).…”

Section: Areas For Improvementsupporting

confidence: 81%

Discerning truth from deception: Human judgments and automation efforts

Rubin

Conroy

2012

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Recent improvements in effectiveness and accuracy of the emerging field of automated deception detection and the associated potential of language technologies have triggered increased interest in mass media and general public. Computational tools capable of alerting users to potentially deceptive content in computer–mediated messages are invaluable for supporting undisrupted, computer–mediated communication and information practices, credibility assessment and decision–making. The goal of this ongoing research is to inform creation of such automated capabilities. In this study we elicit a sample of 90 computer–mediated personal stories with varying levels of deception. Each story has 10 associated human deception level judgments, confidence scores, and explanations. In total, 990 unique respondents participated in the study. Three approaches are taken to the data analysis of the sample: human judges, linguistic detection cues, and machine learning. Comparable to previous research results, human judgments achieve 50–63 percent success rates, depending on what is considered deceptive. Actual deception levels negatively correlate with their confident judgment as being deceptive (r = -0.35, df = 88, ρ = 0.008). The highest-performing machine learning algorithms reach 65 percent accuracy. Linguistic cues are extracted, calculated, and modeled with logistic regression, but are found not to be significant predictors of deception level, confidence score, or an authors’ ability to fool a reader. We address the associated challenges with error analysis. The respondents’ stories and explanations are manually content–analyzed and result in a faceted deception classification (theme, centrality, realism, essence, self–distancing) and a stated perceived cue typology. Deception detection remains novel, challenging, and important in natural language processing, machine learning, and the broader library information science and technology community.

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Section: Areas For Improvementsupporting

confidence: 81%

Discerning truth from deception: Human judgments and automation efforts

Rubin

Conroy

2012

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“…The occasional lie, however, is likely to slip past unnoticed, and most likely goes undetected (or perhaps is uncovered at a much later date, cf. Park, Levine, McCornack, Morrison, & Ferrara, 2002). Alternatively, if most messages are indeed deceptive, then accuracy rates may be very low.…”

Section: Resultsmentioning

confidence: 99%

Deception Detection Accuracy is a Predictable Linear Function of Message Veracity Base-Rate: A Formal Test of Park and Levine's Probability Model

Levine

Kim

Park

et al. 2006

Communication Monographs

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This study provided the first empirical test of point predictions made by the Park-Levine probability model of deception detection accuracy. Participants viewed a series of interviews containing truthful answers, unsanctioned, high-stakes lies, or some combination of both. One randomly selected set of participants (n0/50) made judgments where the probability that each message was honest was P(H)0/.50. Accuracy judgments in this condition were used to generate point predictions generated from the model and tested against the results from a second set of data (n 0/413). Participants were randomly assigned to one of eight base-rate conditions where the probability that a message was honest systematically varied from 0.00 to 1.00. Consistent with the veracity effect, participants in P(H)0/.50 condition were significantly more likely to judge messages as truths than as lies, and consequently truths (67%) were identified with greater accuracy than lies (34%). As predicted by the model, overall accuracy was a linear function of message veracity base-rate, the base-rate induction explained 24% of the variance in accuracy scores, and, on average, raw accuracy scores for specific conditions were predicted to within approximately9/2.6%. The findings show that specific deception detection accuracy scores can be precisely predicted with the Park-Levine model.

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“…Ello lleva a que ciertas personas sean consideradas sospechosas habituales de mentir siendo inocentes y a otras que, mintiendo pero controlando estos indicadores intuitivos, son capaces de aparentar la más incorruptible honestidad. Pero, pese a estas dificultades para captar engaños en un primer momento, existen determinados mecanismos que permiten a las víctimas de engaños desenmascararlos a posteriori -por ejemplo, el testimonio de terceras personas o la evidencia empírica- (Park et al 2010).…”

Section: La Psicología Del Engaño Pseudocientíficounclassified

Los parásitos de la ciencia. Una caracterización psicocognitiva del engaño pseudocientífico

Fasce

2017

THEORIA

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RESUMEN: El marco teórico desde el cual se llevan a cabo investigaciones acerca de la pseudociencia es deficiente, dado que suele incluir otros tipos de creencias carentes de garantía epistémica. En este artículo, se repasarán los mecanismos de explotación de la autoridad científica por parte de la pseudociencia, desarrollando así un marco psicocognitivo más refinado para caracterizar el fenómeno. Se analizará la psicología del engaño pseudocientífico, las raíces cognitivas que posibilitan la epidemiología de este tipo de ideas y sus mecanismos de autolegitimación, como la superioridad dialéctica, el falso apoyo externo o la falsa superioridad ética y/o epistemológica.Palabras clave: Pseudociencia, demarcación, autoridad científica, garantía epistémica.ABSTRACT: The theoretical framework of research on pseudoscience is deficient, since it usually includes other types of beliefs lacking epistemic warranty. In this paper, I review the mechanisms of exploitation of scientific authority by pseudoscience, developing a more refined psycho-cognitive framework to characterize the phenomenon. I will analyze the psychology of pseudoscientific deception, the cognitive roots that enable the epidemiology of these ideas and their mechanisms of self-legitimation, such as dialectical superiority, false external support or false ethical and/or epistemological superiority.Keywords: Pseudoscience, demarcation, scientific authority, epistemic warranty.La pseudociencia constituye una forma de fraude intelectual consistente en ideas ajenas a la ciencia que, en un intento de aumentar su prestigio y aceptación social, se impostan como científicas. Por ello, además de constituir creencias carentes de garantía epistémica, consiste en un engaño con profundas implicaciones sociales y académicas que se ha infiltrado en casi todos los ámbitos de estudio y práctica científica. Esta característica definitoria de la pseudociencia, el impostarse como ciencia, queda plasmada tanto en la propia etimología del término como en el criterio de demarcación propuesto por Sven Ove Hansson (2009;2013), una propuesta demarcativa que, aunque resulta ambigua en su formulación, ofrece un marco de referencia que se corresponde con la definición intuitiva y más habitual de la

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How people really detect lies

Cited by 166 publications

References 28 publications

Discerning truth from deception: Human judgments and automation efforts

Discerning truth from deception: Human judgments and automation efforts

Deception Detection Accuracy is a Predictable Linear Function of Message Veracity Base-Rate: A Formal Test of Park and Levine's Probability Model

Los parásitos de la ciencia. Una caracterización psicocognitiva del engaño pseudocientífico

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