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A Deep Learning Approach for Identification of Arabic Misogyny from Tweets

Online misogyny has become a major cause of concern for Arab women who face gender-based online abuse regularly. Misogyny is a form of hate speech that denigrates a person or a group that identifies as feminine; it is generally described as hatred or contempt towards women. Arab women exposed to many forms of online misogyny, which sadly reinforces and justifies gender inequality, inferior social standing, sexual assault, violence, maltreatment, and underestimating. This paper proposes three methods to identify and classify misogyny behavior from Arabic tweets: (i) BERT, (ii) Ensemble-based model, and (iii) Dense Neural Network-based model. The suggested approach performs admirably on both tasks. The BERT model outperformed the other two suggested methods for misogyny identification, with an accuracy of 0.883, while the ensemble-based approach outperformed the other two suggested methods for misogyny behavior classification task, with an accuracy of 0.764.