  In this paper, an approach for hate speech detection against women in Arabic
community on social media (e.g. Youtube) is proposed. In the literature,
similar works have been presented for other languages such as English. However,
to the best of our knowledge, not much work has been conducted in the Arabic
language. A new hate speech corpus (Arabic\_fr\_en) is developed using three
different annotators. For corpus validation, three different machine learning
algorithms are used, including deep Convolutional Neural Network (CNN), long
short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network.
Simulation results demonstrate the best performance of the CNN model, which
achieved F1-score up to 86\% for the unbalanced corpus as compared to LSTM and
Bi-LSTM.
