ABSTRACT
Internet and social media networks have become a defining factor for modern socialization, communication, and collaboration. At the same time, they have also become a massive platform for antisocial behavior such as cyberbullying, hate speech, and aggressive behavior, among others. This paper examines the automatic detection and classification of cyberbullying incidents in social networks. Most of the current work focuses on the detection of cyberbullying as a binary problem. However, some incidents are more severe than others and demand immediate attention and intervention from the stakeholders such as parents, government, and other relevant agencies. This paper presents the detection and classification of cyberbullying events based on their severity using various deep learning techniques – CNN, recurrent neural networks (RNN-BiLSTM), and HLSTM (HAN-BiLSTM). We curated three datasets from different social networks of varying balances to study the impact of class balance in a multi-class classification problem such as cyberbullying severity detection. Experimental results reveal higher efficiency of HLSTM and RNN-BiLSTM over CNN using all significant metrics such as k-score, MAP, MAR, and F1-Score. Similarly, results demonstrate that the deep learning techniques generally work well with moderately balanced datasets. The study reveals that deep learning techniques effectively detect severity in cyberbullying incidents, a multi-class classification problem.
