在线解决宗教仇恨问题:从分类创建到自动检测
Alan Ramponi1, Benedetta Testa2, Sara Tonelli1
1Fondazione Bruno Kessler, Trento, Italy.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一个新的细粒度标签方案和一个用于在线检测宗教仇恨言论的多语言数据集. 它解决了先前研究的分散性,并探索了跨语言的可转移性,以更好地控制在线滥用的计算.
科学领域:
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
- 检测仇恨言论 检测仇恨言论的检测
背景情况:
- 网上滥用语言,特别是厌恶女性和种族主义,已经得到了广泛的研究.
- 由于分散的数据和不同的注释方案,宗教仇恨言论仍然未被充分探索,这阻碍了有效的计算制.
- 现有的数据集缺乏互操作性,并不能充分解决宗教仇恨言论的特定国家细微差别.
研究的目的:
- 为宗教仇恨言论的检测提出一个细粒度的,可互操作的标签方案.
- 介绍一个新的,注释的Twitter数据集,用英语和意大利语涵盖犹太教,基督教和伊斯兰教.
- 评估各种分类算法,并评估宗教仇恨言论检测的跨语言可转移性.
主要方法:
- 开发一个细粒度的分类系统,用于滥用语言,专门为宗教仇恨言论量身定制.
- 根据拟议的方案,创建和注释英语和意大利语的Twitter数据集.
- 用机器学习和基于变压器的语言模型进行分类任务的实验.
- 使用多语言模型研究跨语言可转移性.
主要成果:
- 证明了拟议的标签方案和宗教仇恨言论检测数据集的有效性.
- 评估了不同分类算法的性能,强调了检测宗教仇恨言论的挑战.
- 展示了在低资源场景中跨语言转移学习的潜力.
结论:
- 拟议的细粒度标签方案和数据集增强了对宗教仇恨言论的研究.
- 这项研究为检测跨语言宗教仇恨言论的计算方法提供了宝贵的见解.
- 这些发现支持开发更强大,更广泛的在线安全工具.
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