Background and Aims: Regulations response to COVID‐19 has increased internet addiction (IA), depression, and pornography addiction (PA) among adolescents worldwide. The objective of this nationwide study was to assess the current prevalence rate of IA, depression, and PA after the post‐COVID‐19 period among school‐going adolescents in Bangladesh.Methods: A total of 8832 male and female adolescents participated in this research. The cross‐sectional study was conducted online using a simple random sampling method. Including the sociodemographic variables, Young's IA Test (IAT‐20) Scale, Patient Health Questionnaire (PHQ‐9), and Pornography Craving Questionnaire (PCQ‐12) were used to measure IA, depression, and PA. By SPSS version 25.0, the prevalence and correlation between IA, depression, and PA were analyzed using the Chi‐square test, binary logistic regression, and a bivariate co‐relation matrix.Results: Sixty‐three percent, 76.6%, and 62.9% of the students were suffering from IA, depression, and PA respectively. Depressive and anxious symptoms were significantly associated with IA. Female students were more depressed than males. Males were more addicted to pornography than females. Students who utilized social media but didn't exercise had greater depression and PA. IA, depression, and PA were correlated.Conclusion: The research emphasizes the need for comprehensive mental health treatments, digital literacy programs, and family and teacher participation to reduce IA, depression, and PA among adolescents post‐COVID‐19. Promotion of physical exercise and supporting policies to build safer online settings for adolescents are also encouraged.

This study examines the exclusion of Vanessa Nakate, a Ugandan climate activist, from a group photo published by the Associated Press (AP) during the 2020 World Economic Forum. Using the muted group theory, it explores the implications of Nakate’s omission as the sole Black individual in the image, shedding light on the broader issue of symbolic annihilation faced by Black women activists in Western media. Through visual and textual analysis, the study reveals the power dynamics and biases behind AP’s decision to crop Nakate, fueling subsequent controversy. Furthermore, the paper examines AP’s response to the backlash, analyzing their strategies to rebuild their reputation, including issuing an apology and releasing the original photograph.

This study presents a systematic review of the scholarly literature on Russia–Ukraine Propaganda on Social Media over the last ten years. This study performs a bibliometric analysis of articles published in the last ten years (2012–2022) and acquired from the Scopus database, followed by a brief content analysis of top articles from leading sources. Furthermore, the study aims to find gaps in the literature and identify the research area that could be developed in this context. VOSviewer application was used for data mining and data visualization from Microsoft Excel. Some interesting facts were found in the bibliometric analysis regarding research and other perspectives. Though the study was related to the propaganda of Russia and Ukraine, the USA is identified as the most attentive country in terms of research and publication on the topic. On the other hand, Russia published many articles regarding its own propaganda on social media.

Background: Due to the increasing consumption of the internet, several outcomes are often seen among adolescents, including an increase in internet addiction (IA) and social media addiction (SMA). It could also have been associated with high body mass index (BMI) and eating disorders (EDs). This study aimed to assess the relationship between BMI, EDs, IA, and SMA among adolescents in Bangladesh and to determine the associated factors. Methodology: Using a cross‐sectional design, this study gathered data from adolescents aged 13–19 years old using an online questionnaire consisting of sociodemographic variables, BMI, eating disorder test scale (EAT‐26), Young's Internet addiction test scale (IAT‐20), and Bergen's social media addiction scale (BSMAS). Apart from the descriptive statistics and Pearson chi‐square test, we employed two sets of binary logistic regression and bivariate co‐relation matrix to analyze the data and to find out the relationship by SPSS 26.0. Results: A total of 2147 adolescents with a mean age of 15.6 years old participated in the study; among them, 70.70% were female. Our study found that 23.2% of participants had EDs, 30.8% of students were addicted to the Internet, and 59.9% of participants were addicted to social media. Concerning BMI, we found that 6.6% of participants were underweight, 1.9% were overweight, 24% were obese, and the remaining 67.5% had a normal BMI. Gender, the purpose of the internet, daily internet use, physical exercise, the study of novels/stories, EDs, and BMI had a significant effect on increasing IA and SMA among adolescents. IA, SMA, and EDs were co‐related. Conclusion: The stakeholders should inspire and take necessary steps to engage the adolescents in physical exercise and literature habits to control the IA and SMA. And regarding BMI and EDs, the parents should be aware of this.

Background: Internet addiction (IA) among adolescents is growing worldwide. Online temptation is particularly strong for adolescents due to rapid physical and cognitive development. IA may impair their mental, emotional, social, and physical health. Few traditional studies were conducted in Bangladesh. Thus, this study aimed to identify adolescents’ IA risk factors using advanced machine learning (ML). Methods: A total of 385 individuals were convenience sampled and surveyed using the Patient Health Questionnaire‐9 (PHQ‐9), the UCLA Loneliness Scale (UCLA‐3), and Young's IA Test (IAT‐20) to measure the prevalence of depression, loneliness, and IA. Boruta found IA prevalence classifying factors. We evaluated decision tree (DT), support vector machine (SVM), logistic regression (LR), and random forest (RF) classification models using confusion matrix, receiver operating characteristic (ROC) curves, and k ‐fold cross‐validation. Results: Among 385 respondents, one‐third (30.1%) reported IA. Participants’ fathers’ education, favorite activity, loneliness, smoking status, depression, and internet use time were selected as important features classifying IA. The performance was tested on the basis of five different classification techniques overall: the SVM linear kernel model (accuracy = 0.819, specificity = 0.869, sensitivity = 0.687, precision = 0.666, area under the ROC curve [AUC] = 0.890, k ‐fold accuracy = 0.801) performed better and authentically classified IA. Conclusion: Raising awareness among adolescents and their parents is crucial because IA is frequent. The ML framework can identify significant prognostic indicators and classify this IA problem more accurately, helping policymakers, stakeholders, and families understand and prevent this crisis by improving policy‐making strategies and counseling services.