The Accuracy-Confidence Correlation in the Detection of Deception

DePaulo, Bella M.; Charlton, Kelly; Cooper, Harris; Lindsay, Jim; Muhlenbruck, Laura

doi:10.1207/s15327957pspr0104_5

Cited by 251 publications

(212 citation statements)

References 43 publications

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“…More often than not, people overestimate their performance, believing that they are much better at a given task than they actually are (Ames & Kammrath, 2004). One possible reason for this inconsistency between performance and appraisal is that individuals might simply be attending to the wrong information (DePaulo, Charlton, Cooper, Lindsay, & Muhlenbruck, 1997). For example, when judging the rapport between two targets, participants use one set of cues to make accurate judgments, but because they are not aware of what these cues are, they instead report that their judgments are based on some other set of false cues (Bernieri, Davis, Rosenthal, & Knee, 1994).…”

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confidence: 99%

“…For example, when judging the rapport between two targets, participants use one set of cues to make accurate judgments, but because they are not aware of what these cues are, they instead report that their judgments are based on some other set of false cues (Bernieri, Davis, Rosenthal, & Knee, 1994). Similarly, in a meta-analysis, DePaulo et al (1997) found that across thousands of decoders, the relationship between accuracy and confidence in detecting deception was essentially nonexistent (r ϭ .04). Thus, there appears to be little relationship between individuals' predicted and actual accuracy, especially for nonverbal behavior.…”

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confidence: 99%

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Accuracy and awareness in the perception and categorization of male sexual orientation.

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Ambady

Adams³

et al. 2008

Journal of Personality and Social Psychology

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For clear and unambiguous social categories, person perception occurs quite accurately from minimal cues. This article addresses the perception of an ambiguous social category (male sexual orientation) from minimal cues. Across 5 studies, the authors examined individuals' actual and self-assessed accuracy when judging male sexual orientation from faces and facial features. Although participants were able to make accurate judgments from multiple facial features (i.e., hair, the eyes, and the mouth area), their perceived accuracy was calibrated with their actual accuracy only when making judgments based on hairstyle, a controllable feature. These findings provide evidence that suggests different processes for extracting social category information during perception: explicit judgments based on obvious cues (hairstyle) and intuitive judgments based on nonobvious cues (information from the eyes and mouth area). Differences in the accuracy of judgments based on targets' controllability and perceivers' awareness of cues provides insight into the processes underlying intuitive predictions and intuitive judgments.

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confidence: 99%

mentioning

confidence: 99%

Accuracy and awareness in the perception and categorization of male sexual orientation.

Rule

Ambady

Adams³

et al. 2008

Journal of Personality and Social Psychology

205

182

View full text Add to dashboard Cite

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“…On average, when scored for accuracy, people succeed only about half of the time (Frank, et al, 2004). In a meta"analytical review of over 100 experiments with over 1,000 participants, DePaulo, et al (1997) determined an unimpressive mean accuracy rate of 54 percent, slightly above chance. In this study, we replicate an overall accuracy since human judges serve as a "gold standard" or a benchmark for comparison in the overall task and comparing the rate of "false positive" answer: are respondents more likely to err when stories are truly truthful, or when they are really deceptive.…”

Section: Lie-truth Discrimination By Human Judgesmentioning

confidence: 99%

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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“…Humans are notoriously poor lie detectors even when they are alerted to the possibility of being lied to (Vrij, 2004, Vrij, 2000, Vrij et al, 2012. A widely cited source that conducted a meta-analytical review of over 100 experiments with over 1,000 participants (DePaulo et al, 1997), concludes that on average people are able to distinguish a lie from a truthful statement with a mean accuracy rate of 54%, slightly above chance (Rubin and Conroy, 2012).…”

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confidence: 99%

Deception Detection and Rumor Debunking for Social Media

Rubin¹

2016

The SAGE Handbook of Social Media Research Methods

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The main premise of this chapter is that the time is ripe for more extensive research and development of social media tools that filter out intentionally deceptive information such as deceptive memes, rumors and hoaxes, fake news or other fake posts, tweets and fraudulent profiles. Social media users' awareness of intentional manipulation of online content appears to be relatively low, while the reliance on unverified information (often obtained from strangers) is at an all-time high. I argue there is need for content verification, systematic fact-checking and filtering of social media streams. This literature survey provides a background for understanding current automated deception detection research, rumor debunking, and broader content verification methodologies, suggests a path towards hybrid technologies, and explains why the development and adoption of such tools might still be a significant challenge. Biographical DetailsVictoria L. Rubin is an Associate Professor at the Faculty of Information and Media Studies and the Director of the Language and Information Technologies Research Lab (LiT.RL) at the University of Western Ontario. She specializes in information retrieval and natural language processing techniques that enable analyses of texts to identify, extract, and organize structured knowledge. She studies complex human information behaviors that are, at least partly, expressed through language such as deception, uncertainty, credibility, and emotions. Her research on Deception Detection has been published in recent core workshops on the topic and prominent information science conferences, as well as the

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The Accuracy-Confidence Correlation in the Detection of Deception

Cited by 251 publications

References 43 publications

Accuracy and awareness in the perception and categorization of male sexual orientation.

Accuracy and awareness in the perception and categorization of male sexual orientation.

Discerning truth from deception: Human judgments and automation efforts

Deception Detection and Rumor Debunking for Social Media

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