A Comparative Analytical Study of Machine Learning Paradigms for Fake News Detection

The quantity of fake news has increased recently due to the quick development of digital media, which explains why automated detection is more relevant than ever. The study conducts a comparative analytical investigation of four machine learning frameworks—classical machine learning, deep learning, transformer-based models, and multimodal models—for the purpose of detecting fake news. Rather than proposing a new model, the study evaluates existing paradigms in terms of performance, interpretability, scalability, and computational complexity. The analysis indicates that advanced models like transformers are more effective in giving a better understanding of the context, but need more computational efforts and have lower interpretability. The research concludes that fake news detection must also be treated as a multi-dimensional system design issue and not necessarily aiming at classification accuracy only.