Cyberbullying Detection and Hate Speech Identification using Machine Learning Techniques

Abstract

Bullying has been prevalent since the beginning of time, It’s just the ways of bullying that have changed over the years, from physical bullying to cyberbullying. According to Williard (2004), there are eight types of cyberbullying such as harassment, denigration, impersonation, etc. It’s been around 2 decades since social media sites came into the picture, but there haven’t been a lot of effective measures to curb social bullying and it has become one of the alarming issues in recent times.Our paper presents an analytical review of cyberbullying detection approaches and assesses methods to recognize hate speech on social media. We aim to apply traditional supervised classification methods as well as some novel ensemble machine learning techniques using a manually annotated open-source dataset for this purpose. This paper does a comparative study of various Supervised algorithms, including standard, as well as ensemble methods. The evaluations of the result based upon the scores obtained by accuracy shows that Ensemble supervised methods have the potential to perform better than traditional supervised methods.