Advanced Spam Detection Techniques in Social Media: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches
P S Akshatha, Syed Muqaddam Abbas, D Vijay, B Suhas, Ramesh Prasad, Sonia D’Souza · 2025
The rapid growth of social media platforms has led to increased spam content, ranging from unwanted advertisements to harmful misinformation, negatively affecting user experience and platform security. Traditional spam detection methods often need to address the evolving complexity of spam tactics. This study explores advanced spam detection techniques using machine learning (ML) and deep learning (DL). We propose a structured framework involving pre-processing, feature extraction, and model training to enhance detection accuracy. We evaluate algorithms such as decision trees, ensemble models, and neural networks, examining their strengths and limitations in classifying spam. Our findings show that advanced ML and DL techniques outperform traditional methods, providing more efficient and accurate spam detection. This study offers valuable insights for improving spam detection systems, ensuring safer social media environments and paving the way for future research combating spam online.