在阿拉伯Xsphere中检测针对女性体育的网络仇恨言论
Fatimah Alhayan1, Monerah Almobarak1, Hawazen Shalabi1
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
PeerJ. Computer science
|July 10, 2024
概括
这项研究开发了一种机器学习模型,用于检测社交媒体上针对沙特女性的基于性别的仇恨言论. 随机森林模型实现了85%的准确性,有助于努力打击在线体育歧视.
科学领域:
- 计算机语言学和社会科学研究专注于在线仇恨言论的检测.
背景情况:
- 沙特女性运动员和粉丝越来越多地使用像X这样的社交媒体平台,使他们暴露在基于性别的仇恨言论和歧视中.
- 针对体育运动中的女性的在线仇恨言论,特别是阿拉伯语,是越来越令人担忧的,研究有限,有效的检测解决方案有限.
研究的目的:
- 探索沙特女性运动员和体育爱好者所经历的网上仇恨言论和性别偏见的形式.
- 提出和评估自动机器学习 (ML) 模型,用于在社交媒体平台上以阿拉伯语检测针对运动中的女性的仇恨言论.
主要方法:
- 研究1:对33名沙特女运动员和体育迷进行半结构面试,以确定仇恨言论的类型.
- 研究2:收集和注释7,487条与体育女性相关的阿拉伯语推特数据集.
- 开发和评估使用术语频率-反向文档频率 (TF-IDF) 和部分语音 (POS) 功能,包括随机森林,用于检测仇恨言论的ML模型.
主要成果:
- 采访显示,普遍存在基于性别的低性言论,厌女主义,以及体育界对女性的外表相关歧视.
- 随机森林模型在检测阿拉伯仇恨言论方面取得了很高的准确性 (85%与TF-IDF,84%与POS),优于其他ML算法.
- 无论是TF-IDF还是POS的特征提取方法都被证明是有效的,用于识别阿拉伯语言中的仇恨言论.
结论:
- 该研究成功地确定了针对运动中的阿拉伯女性的在线仇恨言论的形式,并开发了一种有效的基于ML的检测系统.
- 为女性体育运动创建了一个精心注释的阿拉伯仇恨言论数据集,作为未来研究和干预策略的宝贵资源.
- 这些发现支持沙特阿拉伯的2030年愿景,为女性运动员提供更安全的在线环境,并促进她们更多地参与体育运动.
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