在短视频内容中使用人工智能检测网络欺凌:混合深度学习框架
Ahmad A Mazhar1, Islam Zada2, Manal Aldhayan3
1College of Communication, University of Sharjah, Sharjah, United Arab Emirates.
PloS one
|February 11, 2026
概括
这项研究引入了一种混合深度学习框架,通过分析视觉,音频和文本线索来检测短视频中的网络欺凌. 该模型实现了高精度,为自动化内容调节提供了潜力.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 多媒体分析分析.
背景情况:
- 简短的视频平台 (例如,TikTok,Instagram Reels) 的受欢迎程度激增.
- 网络欺凌在这些平台上是一个日益严重的问题,经常使用多式联络线索.
- 现有的检测方法难以应对基于视频的骚扰的复杂性.
研究的目的:
- 开发一个强大的框架来检测多式联络短视频中的网络欺凌.
- 为应对同步视频,音频和文本数据分析的挑战,以检测骚扰.
- 提高自动化内容调节系统的准确性和可靠性.
主要方法:
- 一种混合深度学习方法,集成卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 网络和变压器编码器.
- 分析空间特征 (CNNs),时间声学模式 (BiLSTM) 和文本内容 (变压器).
- 实现一个语义一致性验证层,用于交叉模式对齐的基于注意力的约束.
主要成果:
- 在基准数据集 (CAVD,SocialVidMix) 上的最新性能,具有高准确性 (91.6%),精确性 (89.7%),回忆力 (93.0%) 和F1得分 (91.3%).
- 在不同的平台上获得一致的结果,包括Instagram,TikTok和YouTube短片.
- 证明了拟议框架的可解释性,稳定性和可扩展性.
结论:
- 混合深度学习框架有效地检测多式联络短视频中的网络欺凌.
- 语义一致性层通过确保交叉模式对齐来增强检测.
- 该框架显示了在自动化内容调节系统中实时部署的巨大潜力.
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