A Survey of Hate Speech Detection for Arabic Social Media: Methods and Datasets
Samar Al-Saqqa, Arafat Awajan, BASSAM H. HAMMO · Procedia Computer Science · 2024
The last decade has witnessed an increase in harmful content on social media. The great proliferation of hate speech and other forms of aggressive language serves as evidence of this trend. The significant growth of user-generated content has prompted researchers to explore advanced techniques for hate speech detection, and most social media platforms have implemented measures to prevent posts targeting individuals or groups based on characteristics such as race, ethnicity, religion, gender, or nationality. This survey summarizes the methods used for hate speech detection in Arabic contexts, focusing on machine learning, deep learning, and transfer learning approaches. Furthermore, it presents Arabic datasets specifically constructed for this task, and identifies existing research gaps, offering insights to guide future studies in this field.