Facial emotion recognition is a crucial process for many applications and is still unresolved. Historically, emotional recognition has usually been achieved through artificial intelligence techniques such as Convolutional Neural Networks. However, this approach is quite expensive in terms of computational power and complexity. To alleviate this problem, we propose a lightweight CNN for facial emotion recognition, called Custom Lightweight CNN-based Model (CLCM), based on the well-known MobileNetV2 architecture. Performance was evaluated on four public datasets, FER-2013, RAF-DB, AffectNet, and CK+, where seven facial emotions were detected. The CLCM model was compared to the well-known MobileNetV2 and ShuffleNetV2 architectures. The CLCM performance results were close to or better than the more complex models. Specifically, in the FER-2013 dataset, CLCM achieved an accuracy of 63%, while MobileNetV2 was 58%, and ShuffleNetV2 65%. In the RAF-DB dataset, CLCM showed 84%, MobileNetV2 73%, and ShuffleNetV2 80%. Lastly, in the AffectNet dataset, MobileNetV2 and ShuffleNetV2 showed an accuracy of 57%, while CLCM showed 54%. The results obtained from this study establish CLCM as one of the efficient models in FER. Although CLCM is a smaller model in terms of parameters (2.3 Million) compared to MobileNetV2 (3.5 Million) and ShuffleNetV2 (3.9 Million), it showed good results in almost all analyses. The reduced CLCM’s computational power allows its application in enhanced human-computer interaction, affective computing, and personalized user experience, especially for real-world scenarios such as psychological and medical assessment, automotive (real-time driver emotional state), and vulnerable individual care where limited resource systems are commonly employed and real-time and reliable response are strongly recommended.

Nowadays, video games are part of our everyday life, and the number of players is increasing each day passing by. Thus, understanding what motivations drive people to play video games is becoming a very important topic for researchers. That is why this systematic review had the objective to summarize the existing literature about gaming motivation by including papers that used a validated tool to do so while excluding those that did address just the psychopathological aspect of gaming. The systematic review was carried out through the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRSIMA). A total of 53 papers were included in this systematic review, and the findings revealed that nonaddicted players and addicted players seem both to play for social, achievement, and competition motivations. Male players appeared more oriented to play to compete with others, while female players seemed to use games for relationship and social reasons. Gaming motivation was stronger in younger people.

Social media (SM) are the new standard for social interaction and people with OCD use such platforms like everyone else. However, the research on these individuals provides limited, sporadic, and difficult-to-generalize data outside of social-media evidence for one specific context concerning how SM is experienced by people with OCD. Our cross-sectional study involved 660 participants (71.4% females, 28.6% males) with 22% of the sample surpassing the 90° percentile threshold to be identified as high-level OCD-symptomatic individuals. Our work highlighted that roughly all OCD types are affected by social media in terms of mood and that these individuals appeared to give SM more importance than non-OCD individuals. The evidence presented, although very narrow, can be conceived as the first building blocks to encourage future research considering how individuals with OCD experience social media, since they appear to be affected more by them compared to non-OCD individuals.

This literature review aims to analyze studies published by researchers on the topic of the relationship between the psychological constructs of the Dark Triad and Cognitive Empathy. This study hypothesizes how having good cognitive empathic skills could benefit people who demonstrate Dark Triad traits, as this could facilitate the implementation of manipulative strategies. Through the process of identifying studies via databases and registers, 23 studies were included in this literature review, and the results and theories brought forward by the researchers find more agreement regarding the individual components of the Dark Triad than the whole construct: narcissism seems to have, for the most part, relatively small and typical positive correlations (more than 50% of correlations), Machiavellianism has relatively small and typical negative relationships (about 80% of correlations), and psychopathy has relatively large negative relationships (about 90% of correlations). This study conveys that Machiavellians and psychopaths, having reduced empathic abilities, use manipulation techniques that do not have to do with empathy (for example seduction, intimidation etc.), while narcissists would be, among these three dimensions, those most likely to understand others’ states of mind and thus be able to use this knowledge to their advantage—although there are doubts about the veracity of the statements and answers given by narcissists in the tests administered to them. This literature review could be a valid aid to professionals dealing with people who exhibit Dark Triad traits; understanding how those exhibiting Dark Triad traits manage their empathic abilities, the areas in which the various dimensions show deficits or not, and how they act to implement their manipulative and controlling tactics could aid in the development of more effective helping strategies to be utilized in therapy settings.

People are known to adjust their behavior based on social information. Starting from 2004, social media rapidly became a new social arena for human interaction, and scholars widely studied the effect of likes on people’s psyche and behavior. However, likes are just one of the possible social feedbacks among many others on social media. Moreover, social feedback influence should be analyzed recognizing individual differences in people’s needs and desires for them. This work was aimed at developing and validating (internally and externally) a scale able to capture people’s perceived need for online social feedback (NfOSF) applicable to most social media platforms. Data coming from 1403 Italian participants were used for this purpose. Exploratory and confirmatory factor analyses appeared to support a two-factor structure for the NfOSF scale, while Pearson’s correlation confirmed the expected positive relations of NfOSF factors with Narcissism, Need to Belong, FOMO, and Social media reputation perception. Eventually, NfOSF scale reliability appeared optimal.