With the proliferation of hate speech on social networks under different
formats, such as abusive language, cyberbullying, and violence, etc., people
have experienced a significant increase in violence, putting them in
uncomfortable situations and threats. Plenty of efforts have been dedicated in
the last few years to overcome this phenomenon to detect hate speech in
different structured languages like English, French, Arabic, and others.
However, a reduced number of works deal with Arabic dialects like Tunisian,
Egyptian, and Gulf, mainly the Algerian ones. To fill in the gap, we propose in
this work a complete approach for detecting hate speech on online Algerian
messages. Many deep learning architectures have been evaluated on the corpus we
created from some Algerian social networks (Facebook, YouTube, and Twitter).
This corpus contains more than 13.5K documents in Algerian dialect written in
Arabic, labeled as hateful or non-hateful. Promising results are obtained,
which show the efficiency of our approach.

我们提出了一种针对在线阿尔及利亚信息的检测仇恨言论的完整方法，并在阿尔及利亚方言的语料库上评估了许多深度学习架构，结果表明我们的方法的效果显著。