Subject Review: Cyberbullying and Detection Methods

Abstract

Cyberbullying is a prevalent issue on social media, causing significant mental and social harm to victims. This review highlights cutting-edge cyberbullying detection technologies, concentrating on machine learning (ML), deep learning (DL), and natural language processing (NLP). The learners are categorized into supervised, unsupervised, and hybrid models, highlighting their pros and cons. This article covers common research datasets, including social media comments, and addresses difficulties including data imbalance, linguistic diversity, and context interpretation. Future efforts include developing context-sensitive models, improving on-the-fly detection, and addressing ethical concerns in automated system deployment.