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Crowdfunding platforms offer founders the possibility to collect funding for project realization. With the advent of these platforms, the risk of fraud has risen. Fraudulent founders provide inaccurate information or pretend interest toward a project. Within this study, we propose deception detection support mechanisms to address this novel type of Internet fraud. We analyze a sample of fraudulent and nonfraudulent projects published at a leading crowdfunding platform. We examine whether the analysis of dynamic communication during the funding period is valuable for identifying fraudulent behavior-apart from analyzing only the static information related to the project. We investigate whether content-based cues and linguistic cues are valuable for fraud detection. The selection of cues and the subsequent feature engineering is based on theories in areas of communication, psychology, and computational linguistics. Our results should be helpful to the stakeholders of crowdfunding platforms and researchers of fraud detection.

The value and credibility of online consumer reviews are compromised by significantly increasing yet difficult-to-identify fake reviews. Extant models for automated online fake review detection rely heavily on verbal behaviors of reviewers while largely ignoring their nonverbal behaviors. This research identifies a variety of nonverbal behavioral features of online reviewers and examines their relative importance for the detection of fake reviews in comparison to that of verbal behavioral features. The results of an empirical evaluation using real-world online reviews reveal that incorporating nonverbal features of reviewers can significantly improve the performance of online fake review detection models. Moreover, compared with verbal features, nonverbal features of reviewers are shown to be more important for fake review detection. Furthermore, model pruning based on a sensitivity analysis improves the parsimony of the developed fake review detection model without sacrificing its performance.

Computer-mediated deception threatens the security of online users' private and personal information. Previous research confirms that humans are bad lie detectors, while demonstrating that certain observable linguistic features can provide crucial cues to detect deception. We designed and conducted an experiment that creates spontaneous deception scenarios in an interactive online game environment. Logistic regression, and certain classification methodologies were applied to analyzing data collected during fall 2014 through spring 2015. Our findings suggest that certain language-action cues (e.g., cognitive load, affective process, latency, and wordiness) reveal patterns of information behavior manifested by deceivers in spontaneous online communication. Moreover, computational approaches to analyzing these language-action cues can provide significant accuracy in detecting computer-mediated deception.

Safeguarding organizations against opportunism and severe deception in computer-mediated communication (CMC) presents a major challenge to chief information officers and information technology managers. New insights into linguistic cues of deception derive from the speech acts innate to CMC. Applying automated text analysis to archival e-mail exchanges in a CMC system as part of a reward program, we assess the ability of word use (micro level), message development (macro level), and intertextual exchange cues (meta level) to detect severe deception by business partners. We empirically assess the predictive ability of our framework using an ordinal multilevel regression model. Results indicate that deceivers minimize the use of referencing and self-deprecation but include more superfluous descriptions and flattery. Deceitful channel partners also overstructure their arguments and rapidly mimic the linguistic style of the account manager across dyadic e-mail exchanges. Thanks to its diagnostic value, the proposed framework can support firms' decision making and guide compliance monitoring system development.

Eye-tracking technology has exhibited promise for identifying deception in automated screening systems. Prior deception research using eye trackers has focused on the detection and interpretation of brief oculometric variations in response to stimuli (e.g., specific images or interview questions). However, more research is needed to understand how variations in oculometric behaviors evolve over the course of an interaction with a deception detection system. Using latent growth curve modeling, we tested hypotheses explaining how two oculometric behaviors-pupil dilation and eye-gaze fixation patterns-evolve over the course of a system interaction for three groups of participants: deceivers who see relevant stimuli (i.e., stimuli pertinent to their deception), deceivers who do not see relevant stimuli, and truth-tellers. The results indicate that the oculometric indicators of deceivers evolve differently over the course of an interaction, and that these trends are indicative of deception regardless of whether relevant stimuli are shown.

Both the ability to deceive others, and the ability to detect deception, has long been proposed to confer an evolutionary advantage. Deception detection has been studied extensively, and the finding that typical individuals fare little better than chance in detecting deception is one of the more robust in the behavioral sciences. Surprisingly, little research has examined individual differences in lie production ability. As a consequence, as far as we are aware, no previous study has investigated whether there exists an association between the ability to lie successfully and the ability to detect lies. Furthermore, only a minority of studies have examined deception as it naturally occurs; in a social, interactive setting. The present study, therefore, explored the relationship between these two facets of deceptive behavior by employing a novel competitive interactive deception task (DeceIT). For the first time, signal detection theory (SDT) was used to measure performance in both the detection and production of deception. A significant relationship was found between the deception-related abilities; those who could accurately detect a lie were able to produce statements that others found difficult to classify as deceptive or truthful. Furthermore, neither ability was related to measures of intelligence or emotional ability. We, therefore, suggest the existence of an underlying deception-general ability that varies across individuals.

This Focused Review expands upon our original paper (You can't kid a kidder\": Interaction between production and detection of deception in an interactive deception task. Frontiers in Human Neuroscience, 6:87). In that paper we introduced a new socially interactive, laboratory-based task, the Deceptive Interaction Task (DeceIT), and used it to measure individuals' ability to lie, their ability to detect the lies of others, and potential individual difference measures contributing to these abilities. We showed that the two skills were correlated; better liars made better lie detectors (a \"deception general\" ability) and this ability seemed to be independent of cognitive (IQ) and emotional (EQ) intelligence. Here, following the Focused Review format, we outline the method and results of the original paper and comment more on the value of lab-based experimental studies of deception, which have attracted criticism in recent years. While acknowledging that experimental paradigms may fail to recreate the full complexity and potential seriousness of real-world deceptive behavior, we suggest that lab-based deception paradigms can offer valuable insight into ecologically-valid deceptive behavior. The use of the DeceIT procedure enabled deception to be studied in an interactive setting, with motivated participants, and importantly allowed the study of both the liar and the lie detector within the same deceptive interaction. It is our thesis that by addressing deception more holistically-by bringing the liar into the \"spotlight\" which is typically trained exclusively on the lie detector-we may further enhance our understanding of deception.

Memory distortions, particularly confabulation, present significant challenges to the accuracy and reliability of polygraph examinations in forensic and clinical settings. This paper investigates how confabulation—the unintentional production of false memories without deliberate intent to deceive—affects credibility assessments and polygraph outcomes. Unlike purposeful deception, confabulation involves individuals genuinely believing their false memories to be accurate, creating a complex interpretative challenge for traditional polygraph methodologies that rely primarily on physiological response patterns. Through a comprehensive review and analytical approach drawing on existing research in memory science, cognitive psychology, and polygraph examination practices, this study reveals that confabulated memories can trigger autonomic responses similar to those elicited by genuine memories, leading to false-positive results. The reconstructive nature of episodic memory processes means that confabulation can occur across all populations—not only in individuals with neurological conditions—and can be influenced by stress, anxiety, suggestive questioning, and cognitive load during examinations. Key findings demonstrate that confabulating individuals often present their false memories with rich sensory–perceptual detail and emotional coherence, making them appear authentic even to experienced professionals. Traditional polygraph methods struggle to distinguish confabulation from intentional deception because contemporary instrumentation and evaluation techniques have remained largely unchanged for decades, relying on approaches that fail to account for the complexity of memory distortions. To address these challenges, the paper proposes several critical improvements: enhanced training for polygraph examiners to recognise confabulation and understand neurological conditions affecting memory-monitoring systems; methodological refinements including pre-test thematic assessments, careful question construction to avoid leading language, and strategic question ordering to reduce associative interference; and the integration of objective multimodal physiological measurements with cognitive interviewing and reality-monitoring techniques. The study concludes that addressing the impact of confabulation on credibility assessment requires an interdisciplinary approach that combines psychophysio-logical measurement with insights from cognitive science and ethical considerations. Such integration is essential for improving the reliability and fairness of polygraph examinations while preventing wrongful accusations based on genuinely believed but false information, thereby maintaining public trust in the justice system.