• Praveen Kumar S
• Pavithra G
• Kavin K
• Saniya Ron J
• Vijaik Raj MT

Cyberbullying is a growing concern on social media, leading to severe psychological and emotional consequences for victims. Traditional methods of detecting cyberbullying rely on manual moderation, which is inefficient and lacks scalability. This paper explores the use of Artificial Intelligence (AI) to automatically detect and classify cyberbullying incidents. By leveraging Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques, AI models can analyze text, detect offensive content, and identify patterns of online harassment. The study evaluates various AI approaches, including transformer-based models like BERT, sentiment analysis, and deep neural networks, to improve detection accuracy. Experimental results demonstrate that AI-driven solutions outperform traditional rule-based detection methods, providing a more effective and scalable approach for maintaining a safer digital environment. Future research will focus on improving contextual understanding, addressing bias in AI models, and integrating multimodal data sources such as images and videos for comprehensive cyberbullying detection.

Index Terms : Cyberbullying Detection, Social Media Safety, Natural Language Processing (NLP),Machine Learning (ML), Artificial Intelligence(AI),Text Classification, Sentiment Analysis, Deep Learning, Hate Speech Detection, Content Moderation, Online Harassment Prevention, Ethics in AI, User Protection, Automated Moderation, Digital Well-being.

"Cyber Bullying Detection System in Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.a755-a758, May-2025, Available :http://www.jetir.org/papers/JETIR2505081.pdf

"Cyber Bullying Detection System in Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppa755-a758, May-2025, Available at : http://www.jetir.org/papers/JETIR2505081.pdf