Building towards Automated Cyberbullying Detection: A Comparative Analysis

Abstract

The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored.

A Review on Deep-Learning-Based Cyberbullying Detection

“…A comparison of automated cyberbullying detection methods, including data annotation, preprocessing, and feature engineering, is presented in the study by Al-Harigy et al [45]. Emoji use in cyberbullying detection and the application of self-supervised learning to annotation are also covered.…”