Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 16 4 2024 10 01 Artificial Intelligence-Driven Cyberbullying Detection: A Survey of Current Techniques 38 63 99050 10.22059/jitm.2024.99050 EN Kholood Alfaleh Department of Information Technology, College of Computer Qassim University Buraydah 51452, Saudi Arabia. Abdulatif Alabdultif Department of Computer Science, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia. Suliman Aladhadh Department of Information Technology, College of Computer Qassim University Buraydah 51452, Saudi Arabia. Journal Article 2024 10 16 Cyberbullying involves using hurtful or offensive language that goes against basic rules of respect and politeness. It harms the online environment and can negatively affect people by causing harassment, discrimination, or emotional pain. To combat this, it is crucial to develop automated methods for detecting and preventing the dissemination of such content. Deep learning, a branch of artificial intelligence, leverages neural networks to learn from data and perform complex tasks, effectively capturing semantic and grammatical nuances to differentiate between abusive and non-abusive language. This survey paper reviews current techniques and advancements in deep learning-based approaches for detecting cyberbullying content on online platforms, aiming to provide a comprehensive understanding of existing methodologies and identify potential avenues for future research to mitigate the spread and impact of such behaviors on the internet. https://jitm.ut.ac.ir/article_99050_7cf51958e1c400d52e8a8e52372bee45.pdf