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

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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)

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.

Suggested Citation

  • Repede Ștefan Emil & Brad Remus, 2025. "A Review of Automatic Fake News Detection: From Traditional Methods to Large Language Models," Future Internet, MDPI, vol. 17(10), pages 1-29, September.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:10:p:435-:d:1758241
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    References listed on IDEAS

    as
    1. Eleftheria Papageorgiou & Christos Chronis & Iraklis Varlamis & Yassine Himeur, 2024. "A Survey on the Use of Large Language Models (LLMs) in Fake News," Future Internet, MDPI, vol. 16(8), pages 1-29, August.
    2. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    3. Izzat Alsmadi & Iyad Alazzam & Mohammad Al-Ramahi & Mohammad Zarour, 2024. "Stance Detection in the Context of Fake News—A New Approach," Future Internet, MDPI, vol. 16(10), pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

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