An Annotated Corpus of Arabic Tweets for Hate Speech Analysis

• URL: http://arxiv.org/abs/2505.11969v2
• Date: Fri, 23 May 2025 02:43:22 GMT
• Title: An Annotated Corpus of Arabic Tweets for Hate Speech Analysis
• Authors: Wajdi Zaghouani, Md. Rafiul Biswas,
• Abstract summary: This study introduces a multilabel hate speech dataset in the Arabic language.<n>We have collected 10000 Arabic tweets and annotated each tweet, whether it contains offensive content or not.
• Score: 0.021665899581403608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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