Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Abstract

The intensive use of the internet comes with negative and positive effects. Cyberbullying is one of the negative effects of using the internet. Cyberbullying has a negative effect on the victims emotionally, academically, and psychologically. Cyberbullying detection tools can help in reducing or eliminating cyberbullying on social media platforms. The aim of the study was to identify the elements that drive cyberbullying and build classification models to determine whether social media textual information contains cyberbullying text or not. The research aim was achieved through a mixed methods research design, containing qualitative and quantitative elements. The drivers of cyberbullying were identified through a literature review. These included age, gender, family structure, parental education, race, technology, anonymity, academic achievement, and awareness of cyber safety. The support vector machines and naïve Bayes models were used to classify the text dataset (Formspring.me dataset), with a 72.81% and a 99.87% classification accuracy, respectively.