Abstract:

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.

Keywords:

Fake News Detection, Deep Learning, Misinformation Detection, Multimodal Analysis, Machine Learning, Transformer Models.

Aim/Objectives

Methods