  This paper addresses the problem of detecting the offensive and abusive
content in Facebook comments, where we focus on the Algerian dialectal Arabic
which is one of under-resourced languages. The latter has a variety of dialects
mixed with different languages (i.e. Berber, French and English). In addition,
we deal with texts written in both Arabic and Roman scripts (i.e. Arabizi). Due
to the scarcity of works on the same language, we have built a new corpus
regrouping more than 8.7k texts manually annotated as normal, abusive and
offensive. We have conducted a series of experiments using the state-of-the-art
classifiers of text categorisation, namely: BiLSTM, CNN, FastText, SVM and NB.
The results showed acceptable performances, but the problem requires further
investigation on linguistic features to increase the identification accuracy.
