The identification of emotions conveyed by faces as genuine or not is a topic under-explored. While some controversial insights are available for happiness (where genuine happiness is supposed to create crow's feet around the eyes), nothing is known regarding other emotions. This topic is important as human beings are known to perform around the chance level to identify genuine emotions. It is thus pivotal to identify the relevant features for the correct identification of a facial emotion as genuine. To this aim, we capitalized on explainable artificial intelligence (XAI), characterized by a superior ability to detect subtle patterns in the data. Two different XAI models were created and applied to a dataset including 50 participants displaying both genuine and not genuine emotions. Results revealed that ML models achieved high accuracies in genuineness discrimination (78 % of average) while being robust and interpretable. The robustness of the results, ensured by their stability across different models and experimental conditions, is critical to improving the generalizability of the results. Our XAI algorithms provided interpretable results as they correctly identified facial muscle movements that are critical for the classification of emotions as genuine or fake.

Malingered responses to psychological testing are frequent when monetary incentives or other forms of rewards are at stake. Psychological symptoms are usually identified through clinical questionnaires which, however, may be easily inflated by malingered responses (fake-bad). A fake-bad response style is usually identified through specialized scales embedded in the personality questionnaires, but no procedure is currently available that reconstructs honest responses from malingered responses. In this paper, we present a technique for the Millon (MCMI-III) questionnaire a widely used test for investigating psychopathology. This technique detects malingered MCMI-III profiles (malingering detector) and removes the intentionally inflated test results (malingering remover). We demonstrate that by applying machine learning to the validity scales of MCMI-III we can discriminate between malingerer and honest profiles with 90% accuracy. Moreover, our results show that by applying regression models to malingerer tests, we are able to well reconstruct the original honest profile. Our models decrease the RMSE (Root Mean Square Error) of the reconstruction up to 19% compared to base correction procedures. Finally, applying the malingering detector to the reconstructed scales, we show that only 9% were classified as malingerers, demonstrating the validity of the proposed approach.

Keystroke dynamics has been recently proved to be an effective behavioral measure to detect subjects who provide false demographic information in online contexts. However, current techniques still suffer from some limits that restrict their practical application, such as the use of errors as a key feature to train the lie detectors and the absence of normalized features. Here, an extension of a keystroke dynamics technique, which was recently proposed to detect faked identities, is reported with the goal to overcome these limitations. Using a Quadratic Discriminant Analysis an accuracy up to 92% in the identification of faked identities has been reached, even if errors were excluded from predictors and normalized features were included. The classification model performs similarly to those previously proposed, with a slightly lower accuracy (−3%) but overcoming their important practical limitations.

Recently, the increasing spread of Online Social Networks (OSNs) provided an unprecedented opportunity of analysing online traces of human behaviour to get insight on individuals and society. Among the others, the possibility of predicting users' political orientation relying on data extracted from OSNs received growing attention. In this study, we introduce and make publicly available a dataset composed of 6.685 unique Twitter users and 9.593.055 Tweets. Differently from most of the dataset currently available in the literature, here, each user was manually labeled according to their political orientation by a pool of human judges, using strict inclusion criteria. Further, we address the feasibility of the automatic classification of Italian Twitter users' political orientation based on their Tweets content. Our analysis focuses first on implementing a series of classifiers with the aim of predicting users' political preference as right- or left-oriented. The built models were then evaluated for inferring the political orientation of those users supporting 'Movimento 5 Stelle' (M5S), an Italian political party with a still unclear political leaning. Results show high performances on the left-right classification task, with accuracy rates up to 93%. Finally, classification performances obtained on M5S supporters and possible applications of our findings are discussed.