• Shyla N
• Keerthana M J
• Anuraag A G
• Khushi Singhal
• Ananya Ankamanal

The detection of hate speech and cyberbullying in social media has become a critical task in today's digital age. The unchecked proliferation of hateful content not only undermines the fabric of our society but also poses serious risks to marginalized communities. This paper presents the implementation of natural language processing (NLP) and convolutional neural networks (CNN) to automatically detect instances of cyberbullying and hate speech on Twitter. By leveraging machine learning techniques, particularly NLP for textual analysis and CNN for image identification, our model aims to accurately classify tweets and posts as either bullying or non-bullying. Utilizing data collected from Twitter API, the proposed system demonstrates promising results in identifying cyberbullying behavior with high accuracy. This research contributes to the ongoing efforts to create more inclusive and safer online environments by developing effective tools for detecting and combating cyberbullying.

"Implementation and Evaluation on Cyber Bullying Detection in Social Networks using Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.p202-p208, May-2024, Available :http://www.jetir.org/papers/JETIR2405G30.pdf

"Implementation and Evaluation on Cyber Bullying Detection in Social Networks using Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppp202-p208, May-2024, Available at : http://www.jetir.org/papers/JETIR2405G30.pdf