Socialmedia has become an essential part of everyday communication, where people share opinions, build friendships, and express themselves. However, this open form of communication has also led to a rise in cyberbullying, where individuals use online platforms to insult, threaten, or emotionally hurt others. Cyberbullying can cause severe psychological stress, anxiety, and even long-term trauma, especially among teenagers and young users. Since millions of posts, comments, images, and messages are posted every second, it is impossible to manually monitor and identify harmful content.
To address this challenge, Machine Learning (ML) offers an automated and effective solution. ML models can learn patterns of abusive language by analyzing large sets of text data and can then recognize similar bullying content in real-time. With the support of Natural Language Processing (NLP), these systems are able to understand the meaning, tone, and context of messages more accurately. This paper discusses how machine learning algorithms can be used to detect cyberbullying on social media platforms, the steps involved in the detection process, and how these systems help create a safer online environment for users. By implementing these techniques, social media platforms can reduce harassment and promote healthier digital communication.