A Review of Automatic Fake News Detection: From Traditional Methods to Large Language Models

Repede Ștefan Emil () and Brad Remus
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Repede Ștefan Emil: Faculty of Engineering, Field of Computer Engineering and Information Technology “Lucian Blaga” University, 550024 Sibiu, Romania
Brad Remus: Faculty of Engineering, Field of Computer Engineering and Information Technology “Lucian Blaga” University, 550024 Sibiu, Romania

Future Internet, 2025, vol. 17, issue 10, 1-29

Abstract: In the current digital era, the spread of fake news presents serious difficulties. This study offers a thorough analysis of recent developments in false news automatic detection techniques, from traditional methods to the most recent developed models like large language models. The review identifies four perspectives on automatic detection of fake news that are oriented towards knowledge, style, propagation, and source of the misinformation. This paper describes how automatic detection methods use data science techniques such as deep learning, large language models, and traditional machine learning. In addition to discussing the shortcomings of existing approaches, such as the absence of datasets, this paper emphasizes the multidimensional function of large language models in creating and identifying fake news while underlining the necessity for textual, visual, and audio common analysis, multidisciplinary collaboration, and greater model transparency.

Keywords: fake news detection; large language models; machine learning; deep learning; fact-checking; misinformation; social media; text classification; multimodal analysis; transformer models (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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