Development of classification algorithm and boosting method for fake news detection: filtration and orientation

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.352117

Keywords:

fake news, social media, infodemy, machine learning, automatic detection of misinformation

Abstract

The object of this study is text-based news content distributed through online media and social media platforms, presented as vector objects formed on the basis of unstructured text data and used for subsequent automated analysis. The problem solved in this study is the limited effectiveness, stability, and generalizing ability of traditional machine learning methods in detecting fake news, especially in conditions of heterogeneous datasets, noisy textual characteristics, and dynamically changing linguistic patterns, which negatively affects the quality of classification.

The article proposes a method for improving the efficiency of machine learning based on the combined use of SVM and the AdaBoost algorithm. To form an informative representation of text data, complex preprocessing and feature extraction using the TF-IDF model are used. The experimental verification of the method was performed on four open datasets: ISOT, Kaggle, News Trends and Reuters.

The results show that the proposed SVM ensemble model with AdaBoost is superior to the basic SVM classifier and a number of traditional algorithms. Accuracy increased from 0.8175 for the base model to 0.83 for SVM+AdaBoost, while memorization increased by 4.02%, average memorization accuracy increased by 2.22%, and the F1 index increased by 1.84%, while the stability of the test accuracy decreased only slightly by 0.19%. The improvement is explained by AdaBoost's adaptive enhancement of the contribution of hard-to-classify objects and a reduction in the number of errors with moderate computational complexity.

The developed approach can be effectively applied in automated systems for monitoring news content and social networks in the presence of marked-up text data and limited computing resources

Author Biographies

Zhanna Suimenbayeva, ALT University

Master of Technology, Head

Office of Innovative Projects

Pramod Kumar Aylapogu, B.V. Raju Institute of Technology

Professor

Department of Electrical Communication Engineering

Ruslan Kassym, ALT University; University of Jaén

Supervisor Project, Researcher

Department of Information and Communication Technologies

Department of Electrical Engineering

Arai Tolegenova, S.Seifullin Kazakh Agrotechnical Research University

PhD, Associate Professor

Department of Information Communication Technologies

Begaidar Sarsekulov, S.Seifullin Kazakh Agrotechnical Research University

Researcher

Department of Information Communication Technologies

Nurlan Suimenbayev, “INSAT Alatau” LLP

Director

Yerdaulet Beibit, ALT University

Researcher

Department of Radio Engineering and Telecommunication

Akmaral Tlenshiyeva, ALT University

Senior Lector

Department Information Communication Technologies

Ayaulym Kassym, Nazarbayev University

Doctoral Student

School of Sciences and Humanities (SSH)

Mussapirova Gulzada, ALT University

Candidate of Technical Sciences, Associate Professor

Department of Radio Engineering and Telecommunication

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Development of classification algorithm and boosting method for fake news detection: filtration and orientation

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Published

2026-02-27

How to Cite

Suimenbayeva, Z., Aylapogu, P. K., Kassym, R., Tolegenova, A., Sarsekulov, B., Suimenbayev, N., Beibit, Y., Tlenshiyeva, A., Kassym, A., & Gulzada, M. (2026). Development of classification algorithm and boosting method for fake news detection: filtration and orientation. Eastern-European Journal of Enterprise Technologies, 1(2 (139), 25–35. https://doi.org/10.15587/1729-4061.2026.352117

Issue

Vol. 1 No. 2 (139) (2026): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2026 Zhanna Suimenbayeva, Pramod Kumar Aylapogu, Ruslan Kassym, Arai Tolegenova, Begaidar Sarsekulov, Nurlan Suimenbayev, Yerdaulet Beibit, Akmaral Tlenshiyeva, Ayaulym Kassym, Mussapirova Gulzada

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