通过先进的检测技术,解决阿拉伯社交媒体中的毒性问题
Loay Hatem1,2, Ahmed Omar3,4, Abdelmgeid A Ali3,4
1Computer Science Department, Faculty of Science, Minia University, Minya, Egypt. loay.hatem@mu.edu.eg.
Scientific reports
|November 21, 2025
概括
研究人员开发了一套新的阿拉伯语数据集,用于检测社交媒体上的有毒内容. 微调的 MARBERTv2 模型实现了高精度,标志着阿拉伯毒性检测的突破.
科学领域:
- 自然语言处理自然语言处理.
- 社交媒体分析 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 在线社交网络促进了沟通,但也举办了仇恨言论和网络欺凌等有毒内容.
- 现有的毒性检测工作主要集中在英语上,忽视其他语言.
- 需要大量资源来解决阿拉伯语的在线虐待问题.
研究的目的:
- 创建一个标准的阿拉伯语数据集,用于在线社交网络 (OSN) 上检测毒性和滥用.
- 在这个新数据集上评估各种机器学习和转移学习模型的性能.
- 推进阿拉伯自然语言处理领域的内容调节.
主要方法:
- 一个新的阿拉伯语数据集的构建和专家注释用于毒性检测.
- 实验十六个机器学习算法,FastText和七个转移学习架构.
- 使用了四种词嵌入技术:词袋 (BOW),术语频率反向文档频率 (TF-IDF),FASTTEXT和来自变压器的双向编码器表示 (BERT).
主要成果:
- 微调的 MARBERTv2 模型与 BERT 嵌入相结合,实现了最高的性能.
- 在分类有毒阿拉伯语内容方面获得了92.43%的F1分数和92.21%的准确性.
- 证明了开发的数据集和阿拉伯毒性检测先进模型的有效性.
结论:
- 这项研究在阿拉伯语中对有毒推文进行分类方面取得了重大突破.
- 强调开发多语言资源以应对在线毒性的重要性.
- 拟议的数据集和 MARBERTv2 模型为阿拉伯语内容调节提供了一个强大的解决方案.
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