Cyber Bullying Detection Using Deep Learning and Natural Language Processing

Social media platforms have become one of the primary communication channels worldwide. Unfortunately, these platforms are also exploited for harmful activities, with cyber bullying being a significant concern, particularly among younger users. This research presents a novel model aimed at detecting instances of cyber bullying using deep learning techniques and Natural language processing (NLP). The research employs three datasets from Twitter, Instagram, and Facebook to predict cyber bullying behaviors through the Long Short-Term Memory (LSTM) algorithm. The findings demonstrate that the proposed model effectively identifies bullying instances, offering improvements over previous detection approaches. The model achieved accuracies of 96.64%, 94.49%, and 91.26% for the Twitter, Instagram, and Facebook datasets, respectively, highlighting its robustness across different social media platforms.

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