Modern approaches for fake news classification
https://doi.org/10.54596/2958-0048-2025-4-195-204
Abstract
This study addresses the problem of classification to determine whether a text is authentic or genuine. It uses state-of-the-art deep learning architectures in natural language processing (NLP), the Bert, Albert and GPT-2 models. Using these advanced models, the study aims to develop accurate and robust classification approaches to effectively distinguish between fake and real news. The test result showed that the proposed method has the potential to be used in distinguishing news that does not contain truth from those that do.
About the Author
A. B. Bilyalova
Kazakh-British Technical University JSC
Kazakhstan
Kazakhstan
Corresponding author, Master of Social Sciences (2016, L.N. Gumilyov Eurasian National University, Astana), “Kazakh-British Technical University” JSC, School of Information Technologies and Engineering.
Almaty
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Review
For citations:
Bilyalova A.B. Modern approaches for fake news classification. Bulletin of Manash Kozybayev North Kazakhstan University. 2025;(4 (68)):195-204. https://doi.org/10.54596/2958-0048-2025-4-195-204
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