Cyberbullying Detection: An Ensemble Based Machine Learning Approach

Research on cyberbullying detection is gaining increasing attention in recent years as both individual victims and societies are greatly affected by it. Moreover, ease of access to social media platforms such as Facebook, Instagram, Twitter, etc. has led to an exponential increase in the mistreatment of people in the form of hateful messages, bullying, sexism, racism, aggressive content, harassment, toxic comment etc. Thus there is an extensive need to identify, control and reduce the bullying contents spread over social media sites, which has motivated us to conduct this research to automate the detection process of offensive language or cyberbullying. Our main aim is to build single and double ensemble-based voting model to classify the contents into two groups: `offensive' or…