A hybrid approach based on personality traits for hate speech detection in Arabic social media

Hossam Elzayady, Mohamed S. Mohamed, Khaled M. Badran, Gouda I. Salama


In recent years, as social media has grown in popularity, people have gained the ability to freely share their views. However, this may lead to users' conflict and hostility, resulting in unattractive online environments. Hate speech relates to using expressions or phrases that are violent, offensive, or insulting to a minority of people. The number of Arab social media users is quickly rising, and this is being followed by an increase in the frequency of cyber hate speech in the area. Therefore, the automated detection of Arabic hate speech has become a major concern for many stakeholders. The intersection of personality learning and hate speech detection is a relatively less studied niche. We suggest a novel approach that is focused on extracting personality trait features and using these features to detect Arabic hate speech. The experimental results show that the proposed approach is superior in terms of the macro-F1 score by achieving 82.3% compared to previous work reported in the literature.


deep learning; hate speech; machine learning; personality traits; text mining;

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DOI: http://doi.org/10.11591/ijece.v13i2.pp1979-1988

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).