• Varun Gupta
• Dr. Saurabh Sharma

The rapid growth of social media platforms has facilitated the widespread dissemination of information, ideas, and opinions. However, this freedom of expression has also given rise to the prevalence of hate speech, which poses significant challenges to maintaining a safe and inclusive online environment. This research paper explores the auto detection of hate speech on social media, aiming to develop automated techniques to identify and mitigate hate speech effectively. The paper discusses various approaches, challenges, and potential solutions in the detection and prevention of hate speech, leveraging natural language processing (NLP), machine learning, and computational linguistics. The findings of this study can aid in the development of robust hate speech detection systems that promote positive online interactions and foster a more inclusive digital space.

Social Media, Hate Speech, Deep Learning, NLP, Machine Learning, API

"Auto Detection of Hate Speech on Social Media Techniques and Challenges", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.e611-e616, July-2023, Available :http://www.jetir.org/papers/JETIR2307465.pdf

"Auto Detection of Hate Speech on Social Media Techniques and Challenges", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppe611-e616, July-2023, Available at : http://www.jetir.org/papers/JETIR2307465.pdf