使用深度学习技术检测网络欺凌:预训练的手套和焦点损失技术
Amr Mohamed El Koshiry1,2, Entesar Hamed I Eliwa3,4, Tarek Abd El-Hafeez4,5
1Department of Curricula and Teaching Methods, College of Education, King Faisal University, Al-Ahsa, Saudi Arabia.
PeerJ. Computer science
|April 25, 2024
概括
这项研究比较了深度学习和经典机器学习用于网络欺凌检测. 焦点损失达到99%的准确性,但大多数模型的回忆力很低,难以识别所有网络欺凌实例.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 网络欺凌构成重大威胁,需要有效的检测方法.
- 机器学习和深度学习为自动化网络欺凌识别提供了有希望的途径.
- 现有的方法需要评估和比较,以确定最佳方法.
研究的目的:
- 调查和比较各种深度学习和经典机器学习技术的有效性,以识别网络欺凌.
- 用标准指标来评估算法性能,例如准确性,精度,回忆和F1分数.
- 提出一个优化的深度学习模型,以加强网络欺凌的检测.
主要方法:
- 文本数据的预处理,包括清理,令牌化,停止和停止文字删除.
- 实施和比较五种经典机器学习算法和三种深度学习模型.
- 使用GloVe字嵌入和焦点损失函数来训练一个卷积神经网络,具有双向长期短期记忆层.
主要成果:
- 焦点损失算法表现出最高的准确度 (99%) 和精度 (86.72%).
- 提出的深度学习模型实现了高准确度 (97.0%与GRU) 和精度 (96.6%与NB).
- 大多数算法的回忆率相对较低,这表明识别所有相关网络欺凌实例存在挑战.
结论:
- 深度学习和经典机器学习技术显示了网络欺凌检测的潜力.
- 拟议的深度学习模型为准确识别网络欺凌提供了一个有希望的方法.
- 需要进一步的研究,以提高全面的网络欺凌检测召回率.
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