用于仇恨言论分析的阿拉伯语推文标注语料库
An Annotated Corpus of Arabic Tweets for Hate Speech Analysis
AI总结：
本研究构建了一个包含10000条阿拉伯语推文的多标签仇恨言论数据集，通过多位标注者标注并计算一致性，评估显示AraBERTv2模型在攻击性内容识别上取得最优性能。
摘要：
由于阿拉伯语方言变体的丰富多样性，识别阿拉伯语中的仇恨言论内容具有挑战性。本研究引入了一个阿拉伯语多标签仇恨言论数据集。我们收集了10000条阿拉伯语推文，并对每条推文进行了标注，判断其是否包含攻击性内容。如果文本包含攻击性内容，我们进一步将其分类为不同的仇恨言论目标，如宗教、性别、政治、民族、出身等。一条文本可以包含单个或多个目标。多位标注者参与了数据标注任务。我们计算了标注者间一致性，攻击性内容的一致性为0.86，多仇恨言论目标的一致性为0.71。最后，我们通过使用不同的基于Transformer的模型评估了数据标注任务，其中AraBERTv2表现最佳，微平均F1得分为0.7865，准确率为0.786。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
