LAUSR

Abstract Researchers around the world have been implementing machine learning as a method to detect cyberbullying text. The machine is trained using features such as variations in texts, through social media context and interactions in a social network environment. The machine can also identify and profile users through gender or use of hate speech. In this study, we analysed different types of mobile applications that manage cyberbullying. This study proposes a mechanism, which combines the best cyberbullying detection features to fill the gaps and limitations of existing applications. The results of the study have shown that the proposed mobile application records a higher accuracy in detecting cyberbully than other available applications.