An application to detect cyberbullying using machine learning and deep learning techniques

Abstract

Nowadays, a lot of people indulge themselves in the world of social media. With the current pandemic scenario, this engagement has only increased as people often rely on social media platforms to express their emotions, find comfort, find like-minded individuals, and form communities. With this extensive use of social media comes many downsides and one of the downsides is cyberbully. Cyberbullying is a form of online harassment that is both unsettling and troubling. It can take many forms, but the most common is a textual format. Cyberbullying is common on social media, and people often end up in a mental breakdown state instead of taking action against the bully. On the majority of social networks, automated detection of these situations necessitates the use of intelligent systems. We have proposed a cyberbullying detection system to address this issue. In this work, we proposed a deep learning framework that will evaluate real-time twitter tweets or social media posts as well as correctly identify any cyberbullying content in them. Recent studies has shown that deep neural network-based approaches are more effective than conventional techniques at detecting cyberbullying texts. Additionally, our application can recognise cyberbullying posts which were written in English, Hindi, and Hinglish (Multilingual data).