• NOORUDDIN SIDDIQUI
• NETAL JAJU
• MOHAMMAD AFFAN KHAN

In parallel to the growing number of platforms for social media purposes providing anonymity, accessibility, and community engagement, the importance of detecting and tracking hateful speech has emerged as an acute societal concern that is likely to affect everyone from individuals to legislators and researchers. Applying automated techniques of hate speech detection and monitoring still does not yield the significant results required to satisfy the existing performance levels, which in turn calls for further research in this area. This review aims to systematically examine and present the state-of-the-art technologies in the fields of Natural Language Processing (NLP) and deep learning (DL), thereafter provide definitions, how they function, and the procedures used with a special focus on the DL architectures. From the methodological standpoint, we were guided by the PRISMA statement to conduct the review including all studies that were published in the last decade and sourced for these from databases such as the ACM Digital Library and via Google Scholar. More so, we critically evaluate existing surveys, discover inherent flaws, and suggest several options for future research in the area of online (hate) speech detection that is both effective and ethical. surveys, identify inherent limitations and propose promising avenues for future research to advance the field of hate speech detection and mitigation effectively and ethically.

"Hate Speech Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.e562-e568, May-2024, Available :http://www.jetir.org/papers/JETIR2405468.pdf

"Hate Speech Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppe562-e568, May-2024, Available at : http://www.jetir.org/papers/JETIR2405468.pdf