This paper provides a comprehensive survey on hate speech detection using natural language processing (NLP). It highlights the growing problem of online hate speech due to the increasing volume of social media content and emphasizes the need for automated detection methods. The survey covers key areas of research, including feature extraction, supervised learning, and the integration of various linguistic and contextual features. It discusses the limitations of current approaches and suggests the need for a benchmark dataset to improve comparability and reliability of research findings. The paper also explores the role of meta-information, multimodal data, and knowledge-based features in enhancing hate speech detection. Finally, it concludes by emphasizing the importance of a multilingual perspective and the need for more sophisticated computational approaches to address the complex nature of hate speech.This paper provides a comprehensive survey on hate speech detection using natural language processing (NLP). It highlights the growing problem of online hate speech due to the increasing volume of social media content and emphasizes the need for automated detection methods. The survey covers key areas of research, including feature extraction, supervised learning, and the integration of various linguistic and contextual features. It discusses the limitations of current approaches and suggests the need for a benchmark dataset to improve comparability and reliability of research findings. The paper also explores the role of meta-information, multimodal data, and knowledge-based features in enhancing hate speech detection. Finally, it concludes by emphasizing the importance of a multilingual perspective and the need for more sophisticated computational approaches to address the complex nature of hate speech.