Cyberbullying detection on twitter using Big Five and Dark Triad features

Abstract

This paper presents a cyberbullying detection model based on user personality, determined by the Big Five and Dark Triad models. The model aims to recognize bullying patterns among Twitter communities, based on relationships between personality traits and cyberbullying. Random Forest, a well-known machine-learning algorithm was used for cyberbullying classification (i.e. aggressor, spammer, bully and normal), applied in conjunction with a baseline algorithm encompassing seven Twitter features (i.e. number of mentions, number of followers and following, popularity, favorite count, status count and number of hash tags). Findings indicate that factoring user's personality greatly improves cyberbullying detection mechanisms. Specifically, extraversion, agreeableness and neuroticism (Big Five), and psychopathy (Dark Triad) were found to be significant in detecting bullies, achieving up to 96% (precision) and 95% (recall). The emergence of significant personality traits in an experimental study supports existing empirical studies that show the relationships between personality traits and cyberbullying.