25 July 2026
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 more sophisticated, driven by Large Language Models (LLMs) and the amplification power of social media.
In this work, EMERGE partners from University of Pisa present a critical assessment of 12 representative fake news detection approaches, spanning traditional machine learning, deep learning, transformers, and specialized cross-domain architectures. They evaluate these methods on 10 publicly available datasets differing in genre, source, topic, and labeling rationale.
Read the paper in the link below.
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