Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms

Cyberbullying has become a major challenge in the digital era, and many people, especially adolescents, use social media platforms to communicate and share information. Some exploit these platforms to embarrass others through messages, e-mails, speech, and public posts, causing severe psychological harm to victims. This study reviews existing research on technologies, approaches, datasets, and evaluation metrics for cyberbullying detection, while highlighting future directions and key challenges. The findings show that traditional models work reasonably well with small datasets but require constant updates; machine learning models face feature extraction and linguistic limitations; deep learning models perform better but lack multilingual and cross-lingual capabilities; and large language models (LLMs) achieve the highest performance, offering flexibility and rich linguistic features but face issues of high-energy use and real-time applicability. Addressing technological, methodological, dataset, and linguistic challenges will improve cyberbullying detection, helping to protect online communication and promote social responsibility.