Lie detection in the future: the online lie detection via human-computer interaction

Half the people in the Planet Earth are now on internet, surfing the web, keeping connection with the outside world, using online services and interacting in social networks. However, the spread of internet is going hand in hand with the growing malicious use of it. Creating fake social network profiles, wide spreading fake news, posting fake reviews, identity theft to perpetuate online financial frauds are only few examples. To face these problems, all the big internet compa-nies, like Google and Facebook, are now taking the direction towards the online lie detection re-search. The present work is a contribution to online deception detection through the study of com-puter-user interaction. After a brief review of the current lie detection methods, focusing on their advantages and disadvantages for online application, a series of proof of concept experiments are reported. Experiments were conducted measuring indices deriving from three different tools of human-computer interaction: reaction times on keyboard, keystroke dynamics and mouse dynam-ics. Two strategies were used to increase liars’ cognitive load and facilitate the observation of distinctive features of deception: unexpected questions and complex questions. Experiments fo-cused on the deception about identity, as it is a very hot issue and represents a current challenge for companies that provide online services. Participants were asked to respond lying or truth tell-ing to questions that appeared on the computer screen, typing the response, clicking on it with the mouse or pressing one of two alternative keys on keyboard. Data collected from liars and truth-tellers’ responses were analyzed and used to train machine learning classification models. Classi-fication accuracies in distinguishing liars from truth-tellers ranged from 80% to 95%, depending on the deceptive task. Results have proved that it is possible to spot liars analyzing their interac-tion with the computer during the act of lie. In particular, we demonstrated that keystroke dynam-ics is a very promising tool for covert lie detection and it is easily integrable with the online exist-ing applications. Moreover, we confirmed that the cognitive complexity of the deceptive task in-creases the possibility to detect deception

False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models

The Relationship between Drug Consumption and Dating App Use: Results from an Italian Survey

To date, the literature regarding the relationship between drug consumption and dating app use is still very scant and inconclusive. The present study was thus aimed at investigating the association between drug consumption and dating app use in the general population. A total of 1278 Italian respondents completed an online ad hoc questionnaire assessing drug consumption (cannabis versus other illicit drugs), dating app use, the primary motive for installing dating apps, and demographics. Multiple logistic regression analyses were run to investigate the role of demographics and dating app use on drug consumption. Being single predicted cannabis use. Using dating apps accounted for higher odds of cannabis use; however, people who intensely used the apps were less likely to consume marijuana. Conversely, dating app use was not associated with the consumption of other drugs. This study suggests the presence of common underlying factors between dating app use and cannabis use, and it highlights the mediating role of the intensity of app use. Conversely, the study suggests that dating app use and the use of other drugs are quite independent behaviors

The Association between Dating Apps and Alcohol Consumption in an Italian Sample of Active Users, Former Users, and Non-Users

To date, the relationship between alcohol use and dating app use has been investigated mostly in conjunction with sexual activities and in homosexual men. For this reason, the aim of this study was to explore the association between dating app use and alcohol consumption among the general population. A cross-sectional study was conducted including app users, non-users, and former users: 1278 respondents completed an online ad hoc questionnaire assessing dating app use, motivations for installing dating apps, alcohol use, and demographics. Multiple logistic regression analysis was run to investigate the association between dating app use, demographic features, and alcohol consumption. Whereas educational level, age, and gender significantly contributed to the regular consumption of alcohol, dating app use did not account for a significant amount of variance between regular and not regular drinkers. However, people who installed and used dating apps with the motivation of searching for sexual partners were more likely to be regular drinkers. Among the active users, heavy app users were less likely to drink regularly. The study indicates that underlying factors (sexual aspects, motives for using the apps) and the intensity of using the apps may mediate the relationship between dating app use and alcohol use

Indicators to distinguish symptom accentuators from symptom producers in individuals with a diagnosed adjustment disorder: A pilot study on inconsistency subtypes using SIMS and MMPI-2-RF

In the context of legal damage evaluations, evaluees may exaggerate or simulate symptoms in an attempt to obtain greater economic compensation. To date, practitioners and researchers have focused on detecting malingering behavior as an exclusively unitary construct. However, we argue that there are two types of inconsistent behavior that speak to possible malingering—accentuating (i.e., exaggerating symptoms that are actually experienced) and simulating (i.e., fabricating symptoms entirely)—each with its own unique attributes; thus, it is necessary to distinguish between them. The aim of the present study was to identify objective indicators to differentiate symptom accentuators from symptom producers and consistent participants. We analyzed the Structured Inventory of Malingered Symptomatology scales and the Minnesota Multiphasic Personality Inventory-2 Restructured Form validity scales of 132 individuals with a diagnosed adjustment disorder with mixed anxiety and depressed mood who had undergone assessment for psychiatric/psychological damage. The results indicated that the SIMS Total Score, Neurologic Impairment and Low Intelligence scales and the MMPI-2-RF Infrequent Responses (F-r) and Response Bias (RBS) scales successfully discriminated among symptom accentuators, symptom producers, and consistent participants. Machine learning analysis was used to identify the most efficient parameter for classifying these three groups, recognizing the SIMS Total Score as the best indicator

The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies

[EN] Objective The present review is intended to provide an up-to-date overview of the strategies available to detect malingered symptoms following whiplash. Whiplash-associated disorders (WADs) represent the most common traffic injuries, having a major impact on economic and healthcare systems worldwide. Heterogeneous symptoms that may arise following whiplash injuries are difficult to objectify and are normally determined based on self-reported complaints. These elements, together with the litigation context, make fraudulent claims particularly likely. Crucially, at present, there is no clear evidence of the instruments available to detect malingered WADs. Methods We conducted a targeted literature review of the methodologies adopted to detect malingered WADs. Relevant studies were identified via Medline (PubMed) and Scopus databases published up to September 2020. Results Twenty-two methodologies are included in the review, grouped into biomechanical techniques, clinical tools applied to forensic settings, and cognitive-based lie detection techniques. Strengths and weaknesses of each methodology are presented, and future directions are discussed. Conclusions Despite the variety of techniques that have been developed to identify malingering in forensic contexts, the present work highlights the current lack of rigorous methodologies for the assessment of WADs that take into account both the heterogeneous nature of the syndrome and the possibility of malingering. We conclude that it is pivotal to promote awareness about the presence of malingering in whiplash cases and highlight the need for novel, high-quality research in this field, with the potential to contribute to the development of standardised procedures for the evaluation of WADs and the detection of malingering.Open access funding provided by Universita degli Studi di Padova within the CRUI-CARE Agreement. This work was supported by funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 777090.Monaro, M.; Baydal Bertomeu, JM.; Zecchinato, F.; Fietta, V.; Sartori, G.; De Rosario Martínez, H. (2021). The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies. International Journal of Legal Medicine. 135(5):2017-2032. https://doi.org/10.1007/s00414-021-02589-wS20172032135

Detecting faking-good response style in personality questionnaires with four choice alternatives

Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80-83%)

A model to differentiate WAD patients and people with abnormal pain behaviour based on biomechanical and self-reported tests

[EN] The prevalence of malingering among individuals presenting whiplash-related symptoms is significant and leads to a huge economic loss due to fraudulent injury claims. Various strategies have been proposed to detect malingering and symptoms exaggeration. However, most of them have been not consistently validated and tested to determine their accuracy in detecting feigned whiplash. This study merges two different approaches to detect whiplash malingering (the mechanical approach and the qualitative analysis of the symptomatology) to obtain a malingering detection model based on a wider range of indices, both biomechanical and self-reported. A sample of 46 malingerers and 59 genuine clinical patients was tested using a kinematic test and a self-report questionnaire asking about the presence of rare and impossible symptoms. The collected measures were used to train and validate a linear discriminant analysis (LDA) classification model. Results showed that malingerers were discriminated from genuine clinical patients based on a greater proportion of rare symptoms vs. possible self-reported symptoms and slower but more repeatable neck motions in the biomechanical test. The fivefold cross-validation of the LDA model yielded an area under the curve (AUC) of 0.84, with a sensitivity of 77.8% and a specificity of 84.7%.Open access funding provided by Universita degli Studi di Padova within the CRUI-CARE Agreement. This work was supported by funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 777090Monaro, M.; De Rosario Martínez, H.; Baydal Bertomeu, JM.; Bernal-Lafuente, M.; Masiero, S.; Macía-Calvo, M.; Cantele, F.... (2021). A model to differentiate WAD patients and people with abnormal pain behaviour based on Biomechanical and self-reported tests. 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Did You Commit a Crime There? Investigating the Visual Exploration Patterns of Guilty, Innocent, Honest, and Dishonest Subjects When Viewing a Complex Mock Crime Scene

Previous studies with the eye-tracking technology have predominantly tracked eye parameters in response to a single simple stimulus, and have generated interesting - sometimes inconsistent - results in research on deceptive behavior. The present study analyzed visual patterns in response to a complex image, to investigate potential differences in eye fixation between guilty versus innocent, and honest versus dishonest participants. One hundred and sixty participants were assigned to one of four experimental groups, defined by the parameters of honesty (dishonesty) and guilt (innocence), and asked to complete a computer-based task, looking at neutral and target images (i.e., images of the mock crime scene). RealEye software was used to capture participants’ eye movements when viewing the images. The findings revealed significant differences in eye movements between the four experimental groups in the pictures in which the area where the crime took place was clearly visible. Dishonest and guilty participants recorded fewer and shorter fixations in the area of the image where the crime took place than those who entered the crime scene but did not commit the crime. No differences between groups emerged in the visual patterns in response to neutral images, confirming that the number and duration of fixations in response to the target images may be attributed to the experimental condition

Idiopathic and acquired pedophilia as two distinct disorders: an insight from neuroimaging

Pedophilia is a disorder of public concern because of its association with child sexual offense and recidivism. Previous neuroimaging studies of potential brain abnormalities underlying pedophilic behavior, either in idiopathic or acquired (i.e., emerging following brain damages) pedophilia, led to inconsistent results. This study sought to explore the neural underpinnings of pedophilic behavior and to determine the extent to which brain alterations may be related to distinct psychopathological features in pedophilia. To this aim, we run a coordinate based meta-analysis on previously published papers reporting whole brain analysis and a lesion network analysis, using brain lesions as seeds in a resting state connectivity analysis. The behavioral profiling approach was applied to link identified regions with the corresponding psychological processes. While no consistent neuroanatomical alterations were identified in idiopathic pedophilia, the current results support that all the lesions causing acquired pedophilia are localized within a shared resting state network that included posterior midlines structures, right inferior temporal gyrus and bilateral orbitofrontal cortex. These regions are associated with action inhibition and social cognition, abilities that are consistently and severely impaired in acquired pedophiles. This study suggests that idiopathic and acquired pedophilia may be two distinct disorders, in line with their distinctive clinical features, including age of onset, reversibility and modus operandi. Understanding the neurobiological underpinnings of pedophilic behavior may contribute to a more comprehensive characterization of these individuals on a clinical ground, a pivotal step forward for the development of more efficient therapeutic rehabilitation strategies