主题对抗神经网络用于跨主题的网络欺凌检测
Shufeng Xiong1, Wenzhuo Liu1, Bingkun Wang2
1College of Information and Management Science, Henan Agricultural University, Zhengzhou, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一个主题对抗神经网络 (TANN),用于在各种在线主题中检测网络欺凌. 通过学习主题不变的特征,TANN提高了检测准确度和稳定性,这对社交媒体安全至关重要.
科学领域:
- 计算语言学 计算语言学
- 社交计算社会计算
- 网络安全 网络安全
背景情况:
- 网络欺凌在社交媒体上是一个重大威胁,各种语言表达方式使检测变得复杂.
- 现有的方法往往无法对不同主题进行概括,因为它们依赖于特定主题的特征.
研究的目的:
- 开发一个新的框架,用于主题不变的网络欺凌检测.
- 提高网络欺凌自动检测系统在各种在线环境中的稳定性和准确性.
主要方法:
- 提出了主题对抗神经网络 (TANN),这是一个端到端的框架,集成了多层次的特征提取,主题歧视器和网络欺凌检测器.
- 采用对抗性培训来解开与主题相关的信息,并保留有害内容的通用语言线索.
- 构建并利用来自中国社交媒体平台 (Weibo,Tieba) 的多主题数据集进行评估.
主要成果:
- 与现有方法相比,TANN在跨主题的网络欺凌检测方面表现优异.
- 该框架显著提高了跨不同主题的网络欺凌检测的稳定性和准确性.
- 成功地减轻了主题干扰,在动态的在线环境中实现了可靠的检测.
结论:
- 坦恩为主题不变的网络欺凌检测提供了一个可扩展和有效的解决方案.
- 拟议的方法通过在各种社交媒体环境中实现可靠的网络欺凌识别,推动了该领域的进步.
- 强调主题不变特征学习对于强大的在线危害检测的重要性.
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