Charfi Anis, Besghaier Mabrouka, Akasheh Raghda, Atalla Andria, Zaghouani Wajdi
Information Systems Department, Carnegie Mellon University, Doha, Qatar.
College of Humanities and Social Sciences, Hamad Bin Khalifa University, Doha, Qatar.
Front Artif Intell. 2024 May 30;7:1391472. doi: 10.3389/frai.2024.1391472. eCollection 2024.
Hate speech detection in Arabic poses a complex challenge due to the dialectal diversity across the Arab world. Most existing hate speech datasets for Arabic cover only one dialect or one hate speech category. They also lack balance across dialects, topics, and hate/non-hate classes. In this paper, we address this gap by presenting ADHAR-a comprehensive multi-dialect, multi-category hate speech corpus for Arabic. ADHAR contains 70,369 words and spans four language variants: Modern Standard Arabic (MSA), Egyptian, Levantine, Gulf and Maghrebi. It covers four key hate speech categories: nationality, religion, ethnicity, and race. A major contribution is that ADHAR is carefully curated to maintain balance across dialects, categories, and hate/non-hate classes to enable unbiased dataset evaluation. We describe the systematic data collection methodology, followed by a rigorous annotation process involving multiple annotators per dialect. Extensive qualitative and quantitative analyses demonstrate the quality and usefulness of ADHAR. Our experiments with various classical and deep learning models demonstrate that our dataset enables the development of robust hate speech classifiers for Arabic, achieving accuracy and F1-scores of up to 90% for hate speech detection and up to 92% for category detection. When trained with Arabert, we achieved an accuracy and F1-score of 94% for hate speech detection, as well as 95% for the category detection.
由于阿拉伯世界方言的多样性，阿拉伯语中的仇恨言论检测面临着复杂的挑战。大多数现有的阿拉伯语仇恨言论数据集只涵盖一种方言或一个仇恨言论类别。它们在方言、主题以及仇恨/非仇恨类别之间也缺乏平衡性。在本文中，我们通过呈现ADHAR来填补这一空白——ADHAR是一个全面的多方言、多类别的阿拉伯语仇恨言论语料库。ADHAR包含70369个单词，涵盖四种语言变体：现代标准阿拉伯语（MSA）、埃及语、黎凡特语、海湾语和马格里布语。它涵盖四个关键的仇恨言论类别：国籍、宗教、种族和民族。一个主要贡献是，ADHAR经过精心策划，以保持方言、类别以及仇恨/非仇恨类别之间的平衡，从而实现无偏差的数据集评估。我们描述了系统的数据收集方法，随后是一个严格的注释过程，每个方言涉及多个注释者。广泛的定性和定量分析证明了ADHAR的质量和实用性。我们使用各种经典和深度学习模型进行的实验表明，我们的数据集能够开发出强大的阿拉伯语仇恨言论分类器，仇恨言论检测的准确率和F1分数高达90%，类别检测的准确率和F1分数高达92%。当使用Arabert进行训练时，我们在仇恨言论检测方面的准确率和F1分数达到了94%，在类别检测方面达到了95%。
