构建首个阿拉伯语多标签仇恨言论数据集，助力方言下仇恨内容识别。
An Annotated Corpus of Arabic Tweets for Hate Speech Analysis
- 收集1万条阿拉伯语推文，标注是否含冒犯性内容及具体攻击目标
- 多标注者一致性达0.86（冒犯性）和0.71（多目标），数据可靠
- 基于AraBERTv2模型测试，微平均F1达0.7865，适用于阿拉伯语仇恨言论研究
由于阿拉伯语方言多样性丰富，识别其中的仇恨言论极具挑战。本研究构建了一个多标签阿拉伯语仇恨言论数据集。共收集10000条阿拉伯语推文，并对每条进行标注：判断是否包含冒犯性内容；若包含，则进一步分类为宗教、性别、政治、民族、籍贯等不同攻击目标，且支持多目标并存。多位标注者参与，计算得冒犯性内容的标注者间一致性为0.86，多目标分类一致性为0.71。最后采用多种基于Transformer的模型评估标注质量，其中AraBERTv2表现最优，微平均F1达到0.7865，准确率为0.786。
原文摘要 · Abstract (English)
Identifying hate speech content in the Arabic language is challenging due to the rich quality of dialectal variations. This study introduces a multilabel hate speech dataset in the Arabic language. We have collected 10000 Arabic tweets and annotated each tweet, whether it contains offensive content or not. If a text contains offensive content, we further classify it into different hate speech targets such as religion, gender, politics, ethnicity, origin, and others. A text can contain either single or multiple targets. Multiple annotators are involved in the data annotation task. We calculated the inter-annotator agreement, which was reported to be 0.86 for offensive content and 0.71 for multiple hate speech targets. Finally, we evaluated the data annotation task by employing a different transformers-based model in which AraBERTv2 outperformed with a micro-F1 score of 0.7865 and an accuracy of 0.786.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。