Comprehensive Analysis of Machine Learning Algorithms for Cyber Hate Detection on Social Media Platform

Cyber bullying and hate mongering are serious problems on social media platforms. With 4.9 billion people using social media worldwide and 398 million in India, unpleasant behavior such as online abuse, hate speech, trolls, and harassment is becoming more and more of a concern. Particularly, professional women and girls at educational institutions face numerous challenges. There are various approaches that researchers are focusing on to alleviate this issue, such as proactive, randomized sampling; robust, automated detections; machine learning; deep learning; and session-based approaches. The increasing demand for secure online communication and digital safety makes this research area essential. Well-known social media sites like Facebook, Instagram, Twitter (X) and YouTube are frequently used for communication and information sharing, but they are sometimes abused to disseminate unpleasant and hateful content. As a result, identifying and stopping hate speech and cyberbullying has grown to be a significant challenge in the domains of digital forensics, artificial intelligence, and cybersecurity. The study of natural language processing and machine learning as a foundation for detection and prevention of cyber bullying and religious content on social media platforms is proposed in this research. Lastly, we identify and talk about the future directions for research in machine learning and natural language processing to combat cyberbullying on social media platforms.