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<p>Cyberbullying can profoundly impact individuals’ mental health, leading to increased feelings of anxiety, depression, and social isolation. Psychological research suggests that cyberbullying victims may experience long-term psychological consequences, including diminished self-esteem and academic performance. The widespread use of social media platforms among university students has raised major concerns over cyberbullying, which can have detrimental effects on student mental well-being and academic performance. The purpose of this study is to develop Cyberbullying Network (CBNet), a novel Convolutional Neural Network (CNN)-based model, and a comprehensive dataset to enhance cyberbullying detection accuracy and real-world applicability in student social media interactions. We designed CBNet, a CNN-based model for detecting cyberbullying among student social media groups. We developed a comprehensive dataset collected from several social media platforms popular among university students. Our results demonstrate that CBNet notably outperforms both the traditional machine learning approaches and the Recurrent Neural Network (RNN)-based model and presents an outstanding value of precision, recall, and F1-score overall, with an Area Under the ROC Curve significantly higher than 0.99. Combined with the fact that the issue of cyberbullying always remains relevant, these results suggest the high feasibility of our suggested approach to the detection of incidents. Given our results, CBNet could be used as a preventative tool for educators, administrators, and community managers to combat cyberbullying behavior and make the online community safer and more welcoming for students. This work suggests the high importance of advanced machine learning approaches to real-world social problems and contributes to the creation of greater digital well-being in university students’ communities. By employing CBNet, institutions can take proactive measures to mitigate the harmful effects of cyberbullying and cultivate a positive online culture conducive to student success and flourishing.</p>

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Utilizing CBNet to effectively address and combat cyberbullying among university students on social media platforms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月15日

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  • Irshad Ahmed Abbasi,
  • Muhammad Shoaib,
  • Mohammed Alshehri,
  • Mohammed Aldawsari

摘要

Cyberbullying can profoundly impact individuals’ mental health, leading to increased feelings of anxiety, depression, and social isolation. Psychological research suggests that cyberbullying victims may experience long-term psychological consequences, including diminished self-esteem and academic performance. The widespread use of social media platforms among university students has raised major concerns over cyberbullying, which can have detrimental effects on student mental well-being and academic performance. The purpose of this study is to develop Cyberbullying Network (CBNet), a novel Convolutional Neural Network (CNN)-based model, and a comprehensive dataset to enhance cyberbullying detection accuracy and real-world applicability in student social media interactions. We designed CBNet, a CNN-based model for detecting cyberbullying among student social media groups. We developed a comprehensive dataset collected from several social media platforms popular among university students. Our results demonstrate that CBNet notably outperforms both the traditional machine learning approaches and the Recurrent Neural Network (RNN)-based model and presents an outstanding value of precision, recall, and F1-score overall, with an Area Under the ROC Curve significantly higher than 0.99. Combined with the fact that the issue of cyberbullying always remains relevant, these results suggest the high feasibility of our suggested approach to the detection of incidents. Given our results, CBNet could be used as a preventative tool for educators, administrators, and community managers to combat cyberbullying behavior and make the online community safer and more welcoming for students. This work suggests the high importance of advanced machine learning approaches to real-world social problems and contributes to the creation of greater digital well-being in university students’ communities. By employing CBNet, institutions can take proactive measures to mitigate the harmful effects of cyberbullying and cultivate a positive online culture conducive to student success and flourishing.