  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.
