The rapid growth of social media has fundamentally reshaped how people communicate, share information, and build relationships in the digital world. Children and teenagers are among the most active users of online platforms, making them particularly vulnerable to cyber harassment, online grooming, and predatory behavior. While social networking services provide opportunities for learning and social interaction, they also create an environment where harmful actors can exploit anonymity and unrestricted access to target minors. The sheer volume of online conversations makes manual monitoring extremely challenging, leaving many harmful interactions undetected until serious damage has already occurred. This research presents an intelligent and scalable framework for detecting child predators and cyber harassment on social media platforms using natural language processing and machine learning techniques. The proposed system focuses on analyzing textual conversations and behavioral communication patterns to identify suspicious intent, abusive language, and grooming strategies. Unlike traditional keyword-based filtering systems, the proposed approach considers linguistic context, sentiment variations, conversational flow, and user interaction patterns to improve detection accuracy and reduce false alarms. The framework incorporates multiple machine learning models to classify conversations into safe and potentially harmful categories. Text preprocessing, feature extraction, and behavioral analysis are combined to capture subtle patterns associated with harassment and grooming. The system is designed to operate in real time, enabling early detection and timely intervention. Alerts generated by the system can assist moderators, parents, and authorities in preventing harmful incidents and protecting vulnerable users.
IJERST (Tue,) studied this question.