A multi-modal panel dataset to understand the psychological impact of the pandemic.

Besides far-reaching public health consequences, the COVID-19 pandemic had a significant psychological impact on people around the world. To gain further insight into this matter, we introduce the Real World Worry Waves Dataset (RW3D). The dataset combines rich open-ended free-text responses with survey data on emotions, significant life events, and psychological stressors in a repeated-measures design in the UK over three years (2020: n = 2441, 2021: n = 1716 and 2022: n = 1152). This paper provides background information on the data collection procedure, the recorded variables, participants' demographics, and higher-order psychological and text-derived variables that emerged from the data. The RW3D is a unique primary data resource that could inspire new research questions on the psychological impact of the pandemic, especially those that connect modalities (here: text data, psychological survey variables and demographics) over time.

Infections in Biological and Targeted Synthetic Drug Use in Rheumatoid Arthritis: Where do We Stand? A Scoping Review and Meta-analysis.

INTRODUCTION: The advent of biological and targeted synthetic therapies has revolutionized rheumatoid arthritis (RA) treatment. However, this has come at the price of an increased risk of infections. The aim of this study was to present an integrated overview of both serious and non-serious infections, and to identify potential predictors of infection risk in RA patients using biological or targeted synthetic drugs. METHODS: We systematically reviewed available literature from PubMed and Cochrane and performed multivariate meta-analysis with meta-regression on the reported infections. Randomized controlled trials and prospective and retrospective observational studies including patient registry studies were analyzed, combined as well as separately. We excluded studies focusing on viral infections only. RESULTS: Infections were not reported in a standardized manner. Meta-analysis showed significant heterogeneity that persisted after forming subgroups by study design and follow-up duration. Overall, the pooled proportions of patients experiencing an infection during a study were 0.30 (95% CI, 0.28-0.33) and 0.03 (95% CI, 0.028-0.035) for any kind of infections or serious infections only, respectively. We found no potential predictors that were consistent across all study subgroups. CONCLUSIONS: The high heterogeneity and the inconsistency of potential predictors between studies show that we do not yet have a complete picture of infection risk in RA patients using biological or targeted synthetic drugs. Besides, we found non-serious infections outnumbered serious infections by a factor 10:1, but only a few studies have focused on their occurrence. Future studies should apply a uniform method of infectious adverse event reporting and also focus on non-serious infections and their impact on treatment decisions and quality of life.

The use-the-best heuristic facilitates deception detection.

Decades of research have shown that people are poor at detecting deception. Understandably, people struggle with integrating the many putative cues to deception into an accurate veracity judgement. Heuristics simplify difficult decisions by ignoring most of the information and relying instead only on the most diagnostic cues. Here we conducted nine studies in which people evaluated honest and deceptive handwritten statements, video transcripts, videotaped interviews or live interviews. Participants performed at the chance level when they made intuitive judgements, free to use any possible cue. But when instructed to rely only on the best available cue (detailedness), they were consistently able to discriminate lies from truths. Our findings challenge the notion that people lack the potential to detect deception. The simplicity and accuracy of the use-the-best heuristic provides a promising new avenue for deception research.

Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin.

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26-69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19-74.17%), or with data-origin (84.44-86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78-88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy.

Cryptocurrencies and future financial crime.

BACKGROUND: Cryptocurrency fraud has become a growing global concern, with various governments reporting an increase in the frequency of and losses from cryptocurrency scams. Despite increasing fraudulent activity involving cryptocurrencies, research on the potential of cryptocurrencies for fraud has not been examined in a systematic study. This review examines the current state of knowledge about what kinds of cryptocurrency fraud currently exist, or are expected to exist in the future, and provides comprehensive definitions of the frauds identified. METHODS: The study involved a scoping review of academic research and grey literature on cryptocurrency fraud and a 1.5-day expert consensus exercise. The review followed the PRISMA-ScR protocol, with eligibility criteria based on language, publication type, relevance to cryptocurrency fraud, and evidence provided. Researchers screened 391 academic records, 106 of which went on to the eligibility phase, and 63 of which were ultimately analysed. We screened 394 grey literature sources, 128 of which passed on to the eligibility phase, and 53 of which were included in our review. The expert consensus exercise was attended by high-profile participants from the private sector, government, and academia. It involved problem planning and analysis activities and discussion about the future of cryptocurrency crime. RESULTS: The academic literature identified 29 different types of cryptocurrency fraud; the grey literature discussed 32 types, 14 of which were not identified in the academic literature (i.e., 47 unique types in total). Ponzi schemes and (synonymous) high yield investment programmes were most discussed across all literature. Participants in the expert consensus exercise ranked pump-and-dump schemes and ransomware as the most profitable and feasible threats, though pump-and-dumps were, notably, perceived as the least harmful type of fraud. CONCLUSIONS: The findings of this scoping review suggest cryptocurrency fraud research is rapidly developing in volume and breadth, though we remain at an early stage of thinking about future problems and scenarios involving cryptocurrencies. The findings of this work emphasise the need for better collaboration across sectors and consensus on definitions surrounding cryptocurrency fraud to address the problems identified.

A repeated-measures study on emotional responses after a year in the pandemic.

The introduction of COVID-19 lockdown measures and an outlook on return to normality are demanding societal changes. Among the most pressing questions is how individuals adjust to the pandemic. This paper examines the emotional responses to the pandemic in a repeated-measures design. Data (n = 1698) were collected in April 2020 (during strict lockdown measures) and in April 2021 (when vaccination programmes gained traction). We asked participants to report their emotions and express these in text data. Statistical tests revealed an average trend towards better adjustment to the pandemic. However, clustering analyses suggested a more complex heterogeneous pattern with a well-coping and a resigning subgroup of participants. Linguistic computational analyses uncovered that topics and n-gram frequencies shifted towards attention to the vaccination programme and away from general worrying. Implications for public mental health efforts in identifying people at heightened risk are discussed. The dataset is made publicly available.

The Grievance Dictionary: Understanding threatening language use.

This paper introduces the Grievance Dictionary, a psycholinguistic dictionary that can be used to automatically understand language use in the context of grievance-fueled violence threat assessment. We describe the development of the dictionary, which was informed by suggestions from experienced threat assessment practitioners. These suggestions and subsequent human and computational word list generation resulted in a dictionary of 20,502 words annotated by 2318 participants. The dictionary was validated by applying it to texts written by violent and non-violent individuals, showing strong evidence for a difference between populations in several dictionary categories. Further classification tasks showed promising performance, but future improvements are still needed. Finally, we provide instructions and suggestions for the use of the Grievance Dictionary by security professionals and (violence) researchers.

How humans impair automated deception detection performance.

BACKGROUND: Deception detection is a prevalent problem for security practitioners. With a need for more large-scale approaches, automated methods using machine learning have gained traction. However, detection performance still implies considerable error rates. Findings from different domains suggest that hybrid human-machine integrations could offer a viable path in detection tasks. METHOD: We collected a corpus of truthful and deceptive answers about participants' autobiographical intentions (n = 1640) and tested whether a combination of supervised machine learning and human judgment could improve deception detection accuracy. Human judges were presented with the outcome of the automated credibility judgment of truthful or deceptive statements. They could either fully overrule it (hybrid-overrule condition) or adjust it within a given boundary (hybrid-adjust condition). RESULTS: The data suggest that in neither of the hybrid conditions did the human judgment add a meaningful contribution. Machine learning in isolation identified truth-tellers and liars with an overall accuracy of 69%. Human involvement through hybrid-overrule decisions brought the accuracy back to chance level. The hybrid-adjust condition did not improve deception detection performance. The decision-making strategies of humans suggest that the truth bias - the tendency to assume the other is telling the truth - could explain the detrimental effect. CONCLUSIONS: The current study does not support the notion that humans can meaningfully add the deception detection performance of a machine learning system. All data are available at https://osf.io/45z7e/.

Being accurate about accuracy in verbal deception detection.

PURPOSE: Verbal credibility assessments examine language differences to tell truthful from deceptive statements (e.g., of allegations of child sexual abuse). The dominant approach in psycholegal deception research to date (used in 81% of recent studies that report on accuracy) to estimate the accuracy of a method is to find the optimal statistical separation between lies and truths in a single dataset. However, this method lacks safeguards against accuracy overestimation. METHOD & RESULTS: A simulation study and empirical data show that this procedure produces overoptimistic accuracy rates that, especially for small sample size studies typical of this field, yield misleading conclusions up to the point that a non-diagnostic tool can be shown to be a valid one. Cross-validation is an easy remedy to this problem. CONCLUSIONS: We caution psycholegal researchers to be more accurate about accuracy and propose guidelines for calculating and reporting accuracy rates.

The first direct replication on using verbal credibility assessment for the detection of deceptive intentions.

Verbal deception detection has gained momentum as a technique to tell truth-tellers from liars. At the same time, researchers' degrees of freedom make it hard to assess the robustness of effects. Replication research can help evaluate how reproducible an effect is. We present the first replication in verbal deception research whereby ferry passengers were instructed to tell the truth or lie about their travel plans. The original study found truth-tellers to include more specific time references in their answers. The replication study that closely mimicked the setting, procedure, materials, coding, and analyses found no lie-truth difference for specific time references. Although the power of our replication study was suboptimal (0.77), Bayesian statistics showed evidence in favor of the null hypothesis. Given the great applied consequences of verbal credibility tests, we hope this first replication attempt ignites much needed preregistered, high-powered, multilab replication efforts.

Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling.

Recently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth-tellers. Experiment 2 examined whether these findings replicated on independent-sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth-tellers' statements. Together, these findings suggest that liars may over-prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data.

Using more different and more familiar targets improves the detection of concealed information.

When embedded among a number of plausible irrelevant options, the presentation of critical (e.g., crime-related or autobiographical) information is associated with a marked increase in response time (RT). This RT effect crucially depends on the inclusion of a target/non-target discrimination task with targets being a dedicated set of items that require a unique response (press YES; for all other items press NO). Targets may be essential because they share a feature - familiarity - with the critical items. Whereas irrelevant items have not been encountered before, critical items are known from the event or the facts of the investigation. Target items are usually learned before the test, and thereby made familiar to the participants. Hence, familiarity-based responding needs to be inhibited on the critical items and may therefore explain the RT increase on the critical items. This leads to the hypothesis that the more participants rely on familiarity, the more pronounced the RT increase on critical items may be. We explored two ways to increase familiarity-based responding: (1) Increasing the number of different target items, and (2) using familiar targets. In two web-based studies (n = 357 and n = 499), both the number of different targets and the use of familiar targets facilitated concealed information detection. The effect of the number of different targets was small yet consistent across both studies, the effect of target familiarity was large in both studies. Our results support the role of familiarity-based responding in the Concealed Information Test and point to ways on how to improve validity of the Concealed Information Test.

Using Named Entities for Computer-Automated Verbal Deception Detection.

There is an increasing demand for automated verbal deception detection systems. We propose named entity recognition (NER; i.e., the automatic identification and extraction of information from text) to model three established theoretical principles: (i) truth tellers provide accounts that are richer in detail, (ii) contain more contextual references (specific persons, locations, and times), and (iii) deceivers tend to withhold potentially checkable information. We test whether NER captures these theoretical concepts and can automatically identify truthful versus deceptive hotel reviews. We extracted the proportion of named entities with two NER tools (spaCy and Stanford's NER) and compared the discriminative ability to a lexicon word count approach (LIWC) and a measure of sentence specificity (speciteller). Named entities discriminated truthful from deceptive hotel reviews above chance level, and outperformed the lexicon approach and sentence specificity. This investigation suggests that named entities may be a useful addition to existing automated verbal deception detection approaches.

Assessing autobiographical memory: the web-based autobiographical Implicit Association Test.

By assessing the association strength with TRUE and FALSE, the autobiographical Implicit Association Test (aIAT) [Sartori, G., Agosta, S., Zogmaister, C., Ferrara, S. D., & Castiello, U. (2008). How to accurately detect autobiographical events. Psychological Science, 19, 772-780. doi: 10.1111/j.1467-9280.2008.02156.x ] aims to determine which of two contrasting statements is true. To efficiently run well-powered aIAT experiments, we propose a web-based aIAT (web-aIAT). Experiment 1 (n = 522) is a web-based replication study of the first published aIAT study [Sartori, G., Agosta, S., Zogmaister, C., Ferrara, S. D., & Castiello, U. (2008). How to accurately detect autobiographical events. Psychological Science, 19, 772-780. doi: 10.1111/j.1467-9280.2008.02156.x ; Experiment 1]. We conclude that the replication was successful as the web-based aIAT could accurately detect which of two playing cards participants chose (AUC = .88; Hit rate = 81%). In Experiment 2 (n = 424), we investigated whether the use of affirmative versus negative sentences may partly explain the variability in aIAT accuracy findings. The aIAT could detect the chosen card when using affirmative (AUC = .90; Hit rate = 81%), but not when using negative sentences (AUC = .60; Hit rate = 53%). The web-based aIAT seems to be a valuable tool to facilitate aIAT research and may help to further identify moderators of the test's accuracy.

Less is more? Detecting lies in veiled witnesses.

Judges in the United States, the United Kingdom, and Canada have ruled that witnesses may not wear the niqab-a type of face veil-when testifying, in part because they believed that it was necessary to see a person's face to detect deception (Muhammad v. Enterprise Rent-A-Car, 2006; R. v. N. S., 2010; The Queen v. D(R), 2013). In two studies, we used conventional research methods and safeguards to empirically examine the assumption that niqabs interfere with lie detection. Female witnesses were randomly assigned to lie or tell the truth while remaining unveiled or while wearing a hijab (i.e., a head veil) or a niqab (i.e., a face veil). In Study 1, laypersons in Canada (N = 232) were more accurate at detecting deception in witnesses who wore niqabs or hijabs than in those who did not wear veils. Concealing portions of witnesses' faces led laypersons to change their decision-making strategies without eliciting negative biases. Lie detection results were partially replicated in Study 2, with laypersons in Canada, the United Kingdom, and the Netherlands (N = 291): observers' performance was better when witnesses wore either niqabs or hijabs than when witnesses did not wear veils. These findings suggest that, contrary to judicial opinion, niqabs do not interfere with-and may, in fact, improve-the ability to detect deception. (PsycINFO Database Record

ID-Check: Online Concealed Information Test Reveals True Identity.

The Internet has already changed people's lives considerably and is likely to drastically change forensic research. We developed a web-based test to reveal concealed autobiographical information. Initial studies identified a number of conditions that affect diagnostic efficiency. By combining these moderators, this study investigated the full potential of the online ID-check. Participants (n = 101) tried to hide their identity and claimed a false identity in a reaction time-based Concealed Information Test. Half of the participants were presented with personal details (e.g., first name, last name, birthday), whereas the others only saw irrelevant details. Results showed that participants' true identity could be detected with high accuracy (AUC = 0.98; overall accuracy: 86-94%). Online memory detection can reliably and validly detect whether someone is hiding their true identity. This suggests that online memory detection might become a valuable tool for forensic applications.

Memory detection 2.0: the first web-based memory detection test.

There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262) tried to hide 2 high salient (birthday, country of origin) and 2 low salient (favourite colour, favourite animal) autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.