Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning

Hemant Kumar Soni

doi:10.31127/tuje.1786498

Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning

Abstract

The spread of fake news on and through social media is a serious concern for all. The fake news provokes communal disturbance, and character assassination leads to volatile financial institutions, too. Hence, it is very much required to design and develop a framework to counter fake news automatically. Such a framework should be scalable and reliable in nature. This work proposes a hybrid framework that includes natural language processing and an ensemble model technique to accurately classify fake news. In the proposed hybrid model, various preprocessing steps, followed by feature selection and applying various machine learning models. At a later stage, a voting classifier is pragmatic to associate predictions from base models. This approach gives a prominent accuracy of 72.96% on the LIAR dataset. This result demonstrates the superiority over traditional models. A comparative analysis of the performance of all applied classifiers is also done and find out the future enhancements of the proposed system.

Keywords

References

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Details

Primary Language

English

Subjects

Information Systems (Other)

Journal Section

Research Article

Authors

Hemant Kumar Soni ^*
0000-0002-8335-6146
India

Early Pub Date

November 16, 2025

Publication Date

December 16, 2025

Submission Date

September 18, 2025

Acceptance Date

November 16, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.31127/tuje.1786498

IZ

https://izlik.org/JA28TY45WW

Cite

RIS / Bibtex

APA

Soni, H. K. (2025). Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning. Turkish Journal of Engineering, 10(1), 222-229. https://doi.org/10.31127/tuje.1786498

AMA

1.Soni HK. Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning. TUJE. 2025;10(1):222-229. doi:10.31127/tuje.1786498

Chicago

Soni, Hemant Kumar. 2025. “Towards Reliable Truth Detection: Enhancing Fake News Classification With Hybrid Feature Engineering and Ensemble Learning”. Turkish Journal of Engineering 10 (1): 222-29. https://doi.org/10.31127/tuje.1786498.

EndNote

Soni HK (December 1, 2025) Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning. Turkish Journal of Engineering 10 1 222–229.

IEEE

[1]H. K. Soni, “Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning”, TUJE, vol. 10, no. 1, pp. 222–229, Dec. 2025, doi: 10.31127/tuje.1786498.

ISNAD

Soni, Hemant Kumar. “Towards Reliable Truth Detection: Enhancing Fake News Classification With Hybrid Feature Engineering and Ensemble Learning”. Turkish Journal of Engineering 10/1 (December 1, 2025): 222-229. https://doi.org/10.31127/tuje.1786498.

JAMA

1.Soni HK. Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning. TUJE. 2025;10:222–229.

MLA

Soni, Hemant Kumar. “Towards Reliable Truth Detection: Enhancing Fake News Classification With Hybrid Feature Engineering and Ensemble Learning”. Turkish Journal of Engineering, vol. 10, no. 1, Dec. 2025, pp. 222-9, doi:10.31127/tuje.1786498.

Vancouver

1.Hemant Kumar Soni. Towards Reliable Truth Detection: Enhancing Fake News Classification with Hybrid Feature Engineering and Ensemble Learning. TUJE. 2025 Dec. 1;10(1):222-9. doi:10.31127/tuje.1786498