Abusive Language Detection on Arabic Social Media

TLDR

The study presents a method for detecting abusive language on Arabic social media. The authors extract obscene words and hashtags via common patterns, classify Twitter users by usage, expand the word list through this classification, and evaluate on a newly created Arabic tweet dataset. They release a freely available Arabic tweet dataset, an expanded list of obscene words and hashtags, and a large corpus of deleted user comments for research.

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.