AI RESEARCH
An Experimental Comparison of the Most Popular Approaches to Fake News Detection
arXiv CS.CL
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ArXi:2603.25501v1 Announce Type: new In recent years, fake news detection has received increasing attention in public debate and scientific research. Despite advances in detection techniques, the production and spread of false information have become sophisticated, driven by Large Language Models (LLMs) and the amplification power of social media. We present a critical assessment of 12 representative fake news detection approaches, spanning traditional machine learning, deep learning, transformers, and specialized cross-domain architectures.