Offensive Language Detection in Arabizi

Detecting offensive language in underresourced languages presents a significant real-world challenge for social media platforms.This paper is the first work focused on the issue of offensive language detection in Arabizi, an under-explored topic in an under-resourced form of Arabic.For the first time, a comprehensive and critical overview of the existing work on the topic is presented.In addition, we carry out experiments using different BERT-like models and show the feasibility of detecting offensive language in Arabizi with high accuracy.Throughout a thorough analysis of results, we emphasize the complexities introduced by dialect variations and out-ofdomain generalization.We use in our experiments a dataset that we have constructed by leveraging existing, albeit limited, resources.To facilitate further research, we make this dataset publicly accessible to the research community.