  The problem of online offensive language limits the health and security of
online users. It is essential to apply the latest state-of-the-art techniques
in developing a system to detect online offensive language and to ensure social
justice to the online communities. Our study investigates the effects of
fine-tuning across several Arabic offensive language datasets. We develop
multiple classifiers that use four datasets individually and in combination in
order to gain knowledge about online Arabic offensive content and classify
users comments accordingly. Our results demonstrate the limited effects of
transfer learning on the classifiers performance, particularly for highly
dialectal comments.
