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Video content on the web platform has increased explosively during the past decade, thanks to the open access to Facebook, YouTube, etc. YouTube is the second-largest social media platform nowadays containing more than 37 million YouTube channels. YouTube revealed at a recent press event that 30,000 new content videos per hour and 720,000 per day are posted. There is a need for an advanced deep learning-based approach to categorize the huge database of YouTube videos. This study aims to develop an artificial intelligence-based approach to categorize YouTube videos. This study analyzes the textual information related to videos like titles, descriptions, user tags, etc. using YouTube exploratory data analysis (YEDA) and shows that such information can be potentially used to categorize videos. A deep convolutional neural network (DCNN) is designed to categorize YouTube videos with efficiency and high accuracy. In addition, recurrent neural network (RNN), and gated recurrent unit (GRU) are also employed for performance comparison. Moreover, logistic regression, support vector machines, decision trees, and random forest models are also used. A large dataset with 9 classes is used for experiments. Experimental findings indicate that the proposed DCNN achieves the highest receiver operating characteristics (ROC) area under the curve (AUC) score of 99% in the context of YouTube video categorization and 96% accuracy which is better than existing approaches. The proposed approach can be used to help YouTube users suggest relevant videos and sort them by video category., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Several studies link adverse childhood experiences (ACEs) to delinquency. Yet, developmental sequalae accounting for this association remain unclear, with previous research limited by cross-sectional research designs and investigations of singular mediating processes. To redress these shortcomings, this study examines the longitudinal association between ACEs and delinquency as mediated by both sleep problems and low self-control, two factors which past research implicates as potentially important for understanding how ACEs contribute to antisocial behavior. Data collected from 480 adolescents (71.3% boys; 86.3% White) and their parents participating in the Michigan Longitudinal Study was used to conduct a serial mediation analysis. The association between ACEs (prior to age 11) and delinquency in late adolescence was found to operate indirectly via sleep problems in early adolescence and low self-control in middle adolescence. Nonetheless, a direct association between ACEs and later delinquency remained. Pathways through which ACEs contribute to later delinquency are complex and multiply determined. Findings indicate that early behavioral interventions, including improving sleep and self-control, could reduce later delinquency. Still, more research is needed to identify additional avenues through which the ACEs-delinquency association unfolds across development.

Collective traumas have a notable impact on adolescent well-being. While some youth face increased risk for mental health problems (e.g., those with maltreatment histories), many demonstrate resilience following traumatic events. One contributing factor to well-being following trauma is the degree to which one isolates from others. Accordingly, we examined the association between maltreatment and internalizing problems during the COVID-19 pandemic as moderated by social isolation. Among adolescents reporting pre-pandemic emotional abuse, those experiencing less isolation reported the lowest levels of anxiety symptoms. Among adolescents reporting pre-pandemic physical abuse, those experiencing less isolation reported the greatest levels of anxiety and depressive symptoms. The findings highlight a public health-oriented approach to youth well-being during collective trauma that extends beyond mitigating disease transmission., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)