Detecting Cyberbullying “Hotspots” on Twitter: A Predictive Analytics Approach

Abstract

The ability to discover cyberbullying “hotspots” on social media is vitally important for purposes of preventing victimization. This study attempts to develop a prediction model for identifying cyberbullying “hotspots” by analyzing the manifestation of charged language on Twitter. A total of 140,000 tweets were collected using a Twitter API during September 2019. The study reports that certain charged language in tweets can indicate a high potential for cyberbullying incidents. Cyberbullies tend to share negative emotion, demonstrate anger, and use abusive words to attack victims. The predictor variables related to “biology,” “sexual,” and “swear” can be further used to differentiate cyberbullies from non-cyberbullies. The study contributes to the detection of cyberbullying “hotspots,” by providing an approach to identify a tendency for cyberbullying activity based on computational analysis of charged language. The contribution is significant for mediation agencies—such as school counseling and law enforcement agencies.