Cyberbullying Detection and Prevention Using Artificial Intelligence

Abstract

With the surge of more social media platforms (SMPs), the problems associated with it have also increased. Cyberbullying is one of the major issues in SMPs. It can be defined as an intentional and rude act which is carried out by a person against another person by making use of electronic forms of contact such as a social media platform. It is the need of the hour to restrict such activities as it leads to severe depressions and even suicide attempts. Detecting online bullying on social media is a challenging task as it takes various forms. In this paper, we have given a summary of the literature survey that we conducted to understand the technologies and challenges in cyberbullying detection. After analysis, we have proposed a BLSTM-based neural architecture to detect and prevent cyberbullying. Based on our observations from the study conducted, a scope for future enhancement is also suggested.