多模式仇恨言论检测:一种用于多语言文本和图像的新型深度学习框架
Furqan Khan Saddozai1, Sahar K Badri2, Daniyal Alghazzawi2
1Gomal Research Institute of Computing, Faculty of Computing, Gomal University, D.I.Khan, KP, Pakistan.
PeerJ. Computer science
|June 26, 2025
概括
这项研究引入了一个深度学习框架,用于检测乌尔都语和英语推文中的多式仇恨言论. 该模型有效地使用文本和图像对仇恨言论进行分类,优于其他方法.
科学领域:
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
- 社交媒体分析 社交媒体分析
背景情况:
- 社交媒体使广泛的意见分享成为可能,但也促进了仇恨言论.
- 在低资源语言中检测多式仇恨言论存在重大挑战.
研究的目的:
- 开发和评估一个深度学习框架,用于对乌尔都语-英语推文中的多式仇恨言论进行分类.
- 引入一个新的数据集,MMHS11K,用于多模式多语言仇恨言论检测.
主要方法:
- 采用了一个深度学习框架,集成双向长期短期记忆 (BiLSTM) 和EfficientNetB1.
- 一个早期的融合策略结合了文本和图像特征进行分类.
- 使用了一组手动注释的数据集,包含11000条多式联络推文 (MMHS11K).
主要成果:
- 在BiLSTM+EfficientNetB1模型中,乌尔都语推文的F1得分为81.2%,英语推文的F1得分为75.5%.
- 拟议的多式联运方法的表现优于单式联运和基线多式联运方法.
结论:
- 开发的框架有效地解决了多语言和多模式仇恨言论检测方面的挑战.
- 这项研究为未来打击网上仇恨言论的进展提供了坚实的基础.
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