Cyberbullying Detection in social media Using Supervised ML & NLP Techniques

Abstract

Abstract: From the day internet came into existence, the era of social networking sprouted. In the beginning, no one may have thought the internet would be a host of numerous amazing services the social networking. Today we can say that online applications and social networking websites have become a non-separable part of one’s life. Many people from diverse age groups spend hours daily on such websites. Despite thoughtlet is emotionally connected through media, these facilities bring along big threats with them such as cyber-attacks, which includes include lying. As social networking sites are increasing, cyberbullying is increasing day by day. To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model that automatically detects social media bullying actions. However, many social media bullying detection techniques have been implemented, but many of them were textual based. Under this background and motivation, it can help to prevent the happen of cyberbullying if we can develop relevant techniques to discover cyberbullying in social media. A machine learning model is proposed to detect and prevent bullying on Twitter. Naïve Bayes is used for training and testing social media bullying content.