Abusive Language Detection on Arabic Social Media

Hamdy Mubarak, Kareem Darwish, Walid Magdy


Abstract
In this paper, we present our work on detecting abusive language on Arabic social media. We extract a list of obscene words and hashtags using common patterns used in offensive and rude communications. We also classify Twitter users according to whether they use any of these words or not in their tweets. We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean). We make this dataset freely available for research, in addition to the list of obscene words and hashtags. We are also publicly releasing a large corpus of classified user comments that were deleted from a popular Arabic news site due to violations the site’s rules and guidelines.
Anthology ID:
W17-3008
Volume:
Proceedings of the First Workshop on Abusive Language Online
Month:
August
Year:
2017
Address:
Vancouver, BC, Canada
Venues:
ALW | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
52–56
Language:
URL:
https://aclanthology.org/W17-3008
DOI:
10.18653/v1/W17-3008
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W17-3008.pdf