A Hybrid Deep Learning Approach for Fake News Detection: Integrating XLNet, FastText, and CNN

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 1, pp. 60–66, January 2026

https://doi.org/10.58482/ijersem.v2i1.9

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

A Hybrid Deep Learning Approach for Fake News Detection: Integrating XLNet, FastText, and CNN

M.A.Manivasagam, M.Ritheesh, Kishan Kumar, Anand Kumar, Devarakonda Praveen Kumar, K Sravan Kumar

Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract

One of the serious trends is that misinformation has escalated a spectre of fake news and its consequences. Solving this challenge requires creating intelligent fake-news detection systems that use the specific algorithms needed to counter the fake-news threat. This paper seeks to improve the fake news detection procedure using the GPT-2 across CNN-LSTM models. For evaluation, we use a Kaggle dataset of real and fake news articles and compare the performance of existing detection algorithms with our recommended integrated architecture. To assess their performance, we fine-tune them on real news and retrain them on fake-news BERT, RoBERTa, FastText, and XLNet. Using GPT-2 for contextual feature extraction, along with CNN or LSTM networks for the other features, we improve classification accuracy over standard baseline models. The performance comparison analysis performed on real and fake news data show that the investigated hybrid model achieves higher accuracy in discriminating between real and fake news than previous models. This corroborates the possibility of integrating generative pre-trained transformers with standard deep learning models to enhance the robustness of a particular misinformation detection system.

Keywords: Fake News Detection, Misinformation, CNN-LSTM, BERT, Contextual Feature Extraction.

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DOI: https://doi.org/10.58482/ijersem.v2i1.9

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Issue 1 (January 2026)

2026-01-31