• Aniket Nalawade
• Soham Bondre
• Atharva Morajkar
• Abhay Muraleedharan

Cyberbullying and hate speech on social media platforms have emerged as major challenges due to their adverse psychological and social impacts. This paper presents an intelligent system for detecting cyberbullying and hate speech in social media content using Natural Language Processing (NLP) and machine learning techniques. The proposed approach analyzes user-generated text collected from platforms such as Reddit, applying advanced preprocessing, sentiment analysis, and feature extraction techniques. A hybrid ensemble model combining XLM-BERT for contextual language understanding and Random Forest for robust classification is employed to classify content into hate speech, cyberbullying, or neutral categories. The system supports multilingual and code-mixed text, improving detection accuracy in diverse linguistic environments. Experimental results demonstrate that the ensemble-based approach enhances recall, precision, and overall classification performance. The proposed solution contributes toward building safer online environments by enabling real-time monitoring, trend analysis, and informed moderation decisions.

Cyberbullying, Hate Speech Detection, Natural Language Processing, Machine Learning, XLM-BERT, Random Forest, Social Media Analysis

"Cyberbullying and Hate Speech Detection using NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.f413-f418, January-2026, Available :http://www.jetir.org/papers/JETIR2601560.pdf

"Cyberbullying and Hate Speech Detection using NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppf413-f418, January-2026, Available at : http://www.jetir.org/papers/JETIR2601560.pdf