利用CBNet有效地解决和打击大学生在社交媒体平台上的网络欺凌问题
Irshad Ahmed Abbasi1, Muhammad Shoaib2, Mohammed Alshehri3
1Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia. aabasy@ub.edu.sa.
Scientific reports
|July 15, 2025
概括
一个新的卷积神经网络 (CNN) 模型,网络欺凌网络 (CBNet),有效地检测到社交媒体上的网络欺凌. 这种先进的AI工具增强了学生的数字福利和在线安全.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 社会学 社会学 社会学
背景情况:
- 网络欺凌严重影响大学生的心理健康,导致焦虑,抑郁和社会孤立.
- 负面的心理后果包括自尊心下降和学业表现受损.
- 学生使用社交媒体加剧了对网络欺凌有害影响的担忧.
研究的目的:
- 开发一种基于卷积神经网络 (CNN) 的新型模型,命名为网络欺凌网络 (CBNet).
- 在学生的社交媒体互动中创建一个全面的数据集,以改善网络欺凌的检测.
- 提高网络欺凌检测系统的准确性和现实世界的适用性.
主要方法:
- 设计了CBNet,一种CNN模型,专门用于在学生社交媒体环境中检测网络欺凌.
- 从大学生经常访问的流行社交媒体平台编制了多样化的数据集.
- 对传统的机器学习和反复神经网络 (RNN) 模型进行评估的CBNet.
主要成果:
- 在网络欺凌检测方面,CBNet显著超过了传统的机器学习和基于RNN的模型.
- 取得了卓越的精度,回忆和F1得分.
- 显示ROC曲线下的面积明显高于0.99,表明检测准确度很高.
结论:
- 网络欺凌网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络网络
- 该模型可以作为教育工作者和管理人员的预防工具,以促进更安全的在线社区.
- 强调先进机器学习对于解决社会问题和促进学生数字福祉的重要性.
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