An Annotated Corpus of Arabic Tweets for Hate Speech Analysis

Wajdi Zaghouani, Md. Rafiul Biswas
5/17/2025
cs.CL

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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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Cite this paper

@article{zaghouani2025annotated,
  title  = {An Annotated Corpus of Arabic Tweets for Hate Speech Analysis},
  author = {Wajdi Zaghouani and Md. Rafiul Biswas},
  year   = {2025},
  eprint = {2505.11969},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.11969v2}
}

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