This study examines aggressive behavior among adolescents in school settings, focusing on its associations with mental health dimensions such as dysfunctional negative emotions and anxiety. A total of 403 adolescents (234 girls and 169 boys) aged 12 and 13 years participated in the study. Self-report questionnaires assessed aggressive tendencies and mental health symptoms, while demographic variables such as age and gender were also collected. Data analysis revealed a non-normal distribution, as determined by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Consequently, non-parametric statistical methods were employed, including the Spearman correlation coefficient to explore relationships between variables and the Mann-Whitney U test to analyze gender differences. The results demonstrated significant positive correlations between aggressive behavior and dysfunctional negative emotions (r = 0.191, p < 0.01) and between aggression and anxiety (r = 0.275, p < 0.01). Additionally, gender differences emerged, with females reporting higher levels of mental health symptoms than males (p < 0.05). These findings highlight the complex relationship between mental health challenges and aggression, emphasizing the significant roles of gender and emotional regulation in shaping these dynamics. The study calls for the development of tailored psychological interventions that not only address aggressive behaviors but also consider the unique mental health needs and emotional profiles of adolescents, ensuring a more personalized and effective approach to support their well-being.

The aim of this study was to make a quantitative contribution to the impact of COVID-19 and Mental on consumer behavior. For this purpose, the data in the Scopus and WoS databases until 5 February 2024 were examined using bibliometric analysis. The data obtained within the scope of this study were classified and analyzed using the VOSviewer program developed for scientific mapping analysis. In the evaluations, 180 studies in the Web of Science database and 371 documents in the Scopus database were identified, and when duplicate studies were combined, 426 studies were included in the analysis. According to the results of the analysis, the journal with the highest number of publications is “Journal of Retailing and Consumer Services”; the organization with the highest number of publications is “Department of management sciences, University of Okara” and “North-West University”; the authors with the highest number of publications and citations are “Wang, Xueqin” and “Yuen, Kum Fai”; and the most cited studies are “Laato et al.” and “Goolsbee and Syverson”. This study provides a comprehensive analysis of the studies on the impact of COVID-19 and mental factors on consumer behavior and makes a qualified contribution to the literature with an important opening.

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.