Combating Online Spam: AI Techniques for Detection, Classification, and Prevention

Abstract

Online spam presents a significant challenge for digital communication and cybersecurity, disrupting user experiences and diminishing the credibility of online platforms. This paper explores various AI techniques for the detection, classification, and prevention of spam, highlighting machine learning algorithms, natural language processing (NLP), and deep learning models. We discuss the effectiveness of supervised and unsupervised learning methods, feature extraction techniques, and the role of user behavior analysis in enhancing spam detection systems. Furthermore, we examine case studies demonstrating the successful implementation of these AI strategies across different platforms, along with the challenges and ethical considerations involved in deploying automated solutions. Ultimately, this research aims to provide a comprehensive framework for leveraging AI technologies to combat online spam effectively.