ANALYSIS OF SCIENTIFIC APPROACHES TO ENSURE HIGH ACCURACY IN FAKE NEWS DETECTION

Authors

  • Uzoqov Lochinbek Mamurjon o‘g‘li Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Doctoral Student,
  • Qurbonov Behzod Bahodir o’g’li PDP University, Senior Lecturer

Keywords:

Fake news, machine learning, deep learning, transformer models, social network analysis, fake news detection algorithms, hybrid models, explainable AI.

Abstract

This article aims to assess the relevance and importance of scientific research and research aimed at improving fake news detection algorithms based on machine learning, deep learning, transformer models and social network analysis. The article assesses the strengths and weaknesses of each research paper and provides recommendations for effective approaches. In addition, by combining scientific approaches, the possibilities of achieving high accuracy and reliable results in identifying fake news are considered.

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Published

2026-01-12

Issue

Vol. 48 (2026)

Section

Articles

License

Copyright (c) 2026 American Journal of Interdisciplinary Research and Development

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

ANALYSIS OF SCIENTIFIC APPROACHES TO ENSURE HIGH ACCURACY IN FAKE NEWS DETECTION. (2026). American Journal of Interdisciplinary Research and Development, 48, 5-10. https://ajird.journalspark.org/index.php/ajird/article/view/1662