Arabic Offensive Language on Twitter: Analysis and Experiments
This work addresses the need for better tools to monitor and mitigate offensive content in Arabic social media, though it is incremental as it focuses on dataset creation and standard methods.
The authors tackled the problem of detecting offensive language on Twitter in Arabic by building a large, unbiased dataset and analyzing its characteristics, achieving an F1 score of 83.2 using state-of-the-art techniques.
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 use offensive language. Lastly, we conduct many experiments to produce strong results (F1 = 83.2) on the dataset using SOTA techniques.