• Gaurav Kumar Yadav
• Sanjay Kumar Pal

The rise of online social media platforms has significantly transformed communication, enabling global connectivity and the free exchange of ideas. However, this transformation has also facilitated the proliferation of hate speech, posing severe challenges to societal harmony and individual well-being. Detecting and predicting hate speech in online social media is a complex task due to the evolving nature of language, context sensitivity, and the subtleties involved in hateful content. Traditional machine learning techniques, while effective to some extent, often struggle with the nuances of hate speech due to their reliance on general-purpose word embeddings. This paper presents a comprehensive survey of hate speech prediction techniques in online social media, emphasizing the use of domain-specific word embeddings.

"Hate Speech Prediction in Online Social Media Using Domain Specific Word Embedding : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c188-c192, November-2024, Available :http://www.jetir.org/papers/JETIR2411225.pdf

"Hate Speech Prediction in Online Social Media Using Domain Specific Word Embedding : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc188-c192, November-2024, Available at : http://www.jetir.org/papers/JETIR2411225.pdf