相关实验视频
利用深度学习来打击社交媒体上的网络欺凌
Mannangatti Vijayarani1, Ganesan Balamurugan2, Radhakrishnan Govindan3
1Department of Mental Health Nursing, ESIC College of Nursing, Bangalore, Karnataka, India.
Industrial psychiatry journal
|November 3, 2025
概括
深度学习模型通过分析文本,图像和用户行为来检测网络欺凌. 尽管存在数据注释和用户匿名等挑战,但这些先进技术提供了更好的网络欺凌检测.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 心理学 心理学 心理学
背景情况:
- 社交媒体使广泛的沟通成为可能,但也促进了网络欺凌,导致严重的情绪和社会伤害.
- 由于用户匿名性和在线互动的复杂性,现有的网络欺凌检测方法不足.
- 深度学习为识别数字内容中的网络欺凌模式提供了先进的功能.
研究的目的:
- 探索深度学习技术的潜力,以有效地检测网络欺凌.
- 突出应用人工智能的最新进展和持续挑战,以打击在线骚扰.
- 强调需要共同努力,开发道德和文化敏感的解决方案.
主要方法:
- 利用基于变压器和循环神经网络来分析文本内容,图像和用户行为.
- 整合多式联络数据,包括表情符号和情绪分析,以提高检测准确度.
- 解决与数据注释,语和上下文依赖的解释相关的挑战.
主要成果:
- 与传统方法相比,深度学习模型在检测各种形式的网络欺凌方面表现出更高的准确性.
- 多式联网数据分析捕获了微妙的在线通信细微差别,改善了对骚扰和威胁的识别.
- 匿名性和取决于背景的语言仍然是识别犯罪者的重大障碍.
结论:
- 深度学习提供了一种强大的网络欺凌检测方法,提供了更全面的分析.
- 克服数据注释,语解释和用户匿名性的挑战对于进一步的进步至关重要.
- 跨学科的合作对于创建有效,道德和全球适用的网络欺凌解决方案至关重要.
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