The rapid expansion of social media platforms has brought about significant advancements in communication but has also created fertile ground for cybercrimes such as cyberbullying, online harassment, hate speech, phishing, and misinformation. Traditional rulebased systems often fail to keep pace with the evolving language and tactics used by cybercriminals. This paper explores the application of Artificial Intelligence (AI), particularly Natural Language Processing (NLP) and Machine Learning (ML), in detecting and mitigating cybercrimes on social media platforms. The study reviews current AI models used for identifying harmful content, user behavior anomalies, and coordinated malicious activity. It also examines challenges such as data privacy, model bias, real-time detection, and multilingual content analysis. The paper proposes a hybrid framework integrating deep learning classifiers with contextual awareness for enhanced detection accuracy. Experimental results on benchmark datasets demonstrate improved precision and recall compared to traditional methods. The findings highlight the importance of AI in ensuring digital safety and underscore the need for continuous model updates to counter evolving cyber threats.
Cybercrime Detection, Social Media Security, Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Online Harassment, Misinformation, Deep Learning, Content Moderation, Real-time Monitoring