Abstract

Social media apps are very common among young people due to the growing use of technologically advanced devices, which may be both beneficial and disadvantageous. This study demonstrates the viability of CNNs in cyberbullying detection through a systematic approach encompassing data preprocessing, analysis, and model training. The results suggest promising outcomes for utilizing deep learning techniques in combating online harassment. Our project's primary goal is to create a tool that uses social media intelligence and analysis methods to proactively detect and lessen cyberthreats across the many stages of product design and development. Cyberbullying is a relatively new phenomena that has affected people of all ages, particularly teenagers, on a socio-psychological level over the last ten years. As digital technology advances, young people are becoming more reliant on social media, which increases the likelihood of cyberbullying.