The rapid advancement of Deep Learning (DL) techniques has led to the widespread proliferation of DeepFake (DF) images, raising real-world concerns. In this work, DeepDect is proposed and evaluated in a real-world scenario. The platform has been developed using a human-centered approach, integrating insights (requirements) from both common and DF expert users. The platform can detect both face-swapping and generated deepfake faces, which are the most common cases of DF in the current context. Two benchmarks have been conducted to evaluate state-of-the-art DF detection models for face-swap and AI-generated images. The best-performing models (ResNet-50 and Random Forest for face-swapping detection, Capsule Forensics v2, and CNN for AI-generated images) have been integrated into the platform as the detection engine. An Explainable AI (XAI) module has been implemented and integrated into DeepDect to provide visual (Grad-CAM heatmaps) and textual explanations, enhancing interpretability and user trust. A real-world evaluation involving 108 participants was performed to assess DeepDect’s effectiveness compared to human detection. DeepDect has achieved 81% detection accuracy, outperforming human users, underscoring the need for such a tool in real-world applications. These findings have highlighted the importance of accessible, explainable, and high-performing AI solutions, offering a balance between technical robustness and User-Centric Design (UCD).

Cyberbullying is an intentional aggressive act carried out repeatedly through electronic means against a victim unable to defend themself. This phenomenon combines aggressive behavior, repetitive actions, and the victim's inability to defend themself with the permanent nature of online content and easy and wide dissemination. This work focuses on cyberaggression, defined as aggressive online behavior that is distinct from cyberbullying because it is not repeated. A new dataset, “Aggressive Italian Dataset,” and a text feature extraction method were developed and compared with various Shallow Learning and BERT models in Italian — the data collected from Twitter (X) and Instagram concern offenses to famous people. The goal is to expand the dataset to include comments from adult users, simulating the fundamental dynamics of online assaults. BERT appears to have lower Precision, Recall, F1-Score, and Accuracy performance than other Shallow models with our innovative Feature Engineering.

Background: Employee well-being is increasingly understood as the result of the inter-action between individual vulnerabilities and organizational resources. However, limited evidence has examined how employees’ emotion regulation difficulties, workplace bullying, and leaders’ emotional intelligence (EI) jointly contribute to psychological well-being within a multilevel organizational context. Drawing on the Job Demands–Resources (JD-R) theory, this study conceptualizes emotion regulation difficulties as indicators of deficits in personal emotional resources, workplace bullying as a psycho-social job demand, and leaders’ EI as a team-level organizational resource. Methods: A multilevel design was adopted involving 287 employees nested within 41 work teams of an Italian communication company. Employees completed measures of emotion regulation difficulties, workplace bullying, and psychological well-being (level 1), whereas team leaders (level 2) completed the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT). Random-intercept multilevel models were estimated to examine direct and cross-level relationships. Results: Greater emotion regulation difficulties and higher exposure to workplace bullying were associated with poorer psychological well-being. Although leaders’ EI showed limited direct effects on well-being, specific emotional abilities—particularly understanding emotions —buffered the negative association between workplace bullying and psychological well-being. These findings suggest that emotionally intelligent leaders can provide a supportive interpersonal context that mitigates the impact of employees’ emotional vulnerabilities. Conclusions: The findings support a multilevel JD-R perspective in which employee well-being emerges from the interaction between deficits in personal emotional re-sources, psychosocial job demands, and organizational resources. By providing partial evidence for the buffering role of specific leader EI abilities, this study extends current knowledge on emotionally intelligent leadership and highlights its value for promoting psychologically healthy and sustainable workplaces.

Background: Employee well-being is increasingly understood as the result of the interaction between individual vulnerabilities and organizational resources. However, limited evidence has examined how employees’ emotion regulation difficulties, workplace bullying, and leaders’ emotional intelligence (EI) jointly contribute to psychological well-being within a multilevel organizational context. Drawing on the Job Demands–Resources (JD-R) theory, this study conceptualizes emotion regulation difficulties as indicators of deficits in personal emotional resources, workplace bullying as a psychosocial job demand, and leaders’ EI as a team-level organizational resource. Methods: A multilevel design was adopted involving 287 employees nested within 41 work teams of an Italian communication company. Employees completed measures of emotion regulation difficulties, workplace bullying, and psychological well-being (level 1), whereas team leaders (level 2) completed the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT). Random-intercept multilevel models were estimated to examine direct and cross-level relationships. Results: Greater emotion regulation difficulties and higher exposure to workplace bullying were associated with poorer psychological well-being. Although leaders’ EI showed limited direct effects on well-being, specific emotional abilities—particularly understanding emotions —buffered the negative association between workplace bullying and psychological well-being. These findings suggest that emotionally intelligent leaders can provide a supportive interpersonal context that mitigates the impact of employees’ emotional vulnerabilities. Conclusions: The findings support a multilevel JD-R perspective in which employee well-being emerges from the interaction between deficits in personal emotional resources, psychosocial job demands, and organizational resources. By providing partial evidence for the buffering role of specific leader EI abilities, this study extends current knowledge on emotionally intelligent leadership and highlights its value for promoting psychologically healthy and sustainable workplaces.