Arabic Offensive Language on Twitter: Analysis and Experiments

Hamdy Mubarak, Ammar Rashed, Kareem Darwish, Younes Samih, Ahmed Abdelali


Abstract
Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers useoffensive language. Lastly, we conduct many experiments to produce strong results (F1 =83.2) on the dataset using SOTA techniques.
Anthology ID:
2021.wanlp-1.13
Volume:
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Month:
April
Year:
2021
Address:
Kyiv, Ukraine (Virtual)
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
126–135
Language:
URL:
https://aclanthology.org/2021.wanlp-1.13
DOI:
Bibkey:
Cite (ACL):
Hamdy Mubarak, Ammar Rashed, Kareem Darwish, Younes Samih, and Ahmed Abdelali. 2021. Arabic Offensive Language on Twitter: Analysis and Experiments. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 126–135, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
Cite (Informal):
Arabic Offensive Language on Twitter: Analysis and Experiments (Mubarak et al., WANLP 2021)
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PDF:
https://aclanthology.org/2021.wanlp-1.13.pdf
Data
RACE