Citation

Sneha, Soily Ghosh and Sen, Anik and Malik, Sumaiya and Uddin, Ashraf and Hossen, Md. Jakir (2026) BiLSTM-LIME: integrating NLP and advanced machine learning models for fake news detection. Discover Artificial Intelligence, 6 (1). ISSN 2731-0809

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Abstract

The proliferation of fake news on digital platforms poses a growing threat to democratic integrity, public trust, and social stability. Addressing the limitations of existing detection models, particularly their opacity and computational overhead, this study proposes BiLSTM-LIME, a hybrid framework integrating Bidirectional Long Short-Term Memory (BiLSTM) networks with Local Interpretable Model Agnostic Explanations (LIME) for interpretable and efficient fake news detection. The model employs pre-trained GloVe embeddings to capture semantic and syntactic dependencies, while LIME provides token level explainability, enhancing transparency in model decisions. A standardized English corpus with a refined preprocessing pipeline was developed to ensure robust and reproducible evaluation. Experimental results demonstrate that the proposed BiLSTM-LIME model achieves 97.21% accuracy and an F1 score of 0.97, outperforming several state of the art transformer based and multimodal approaches while maintaining a significantly lower computational cost. The framework establishes a balance between performance and interpretability, offering a scalable, transparent, and resource efficient solution for real world fake news detection.

Item Type:	Article
Uncontrolled Keywords:	Explainable AI
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Feb 2026 08:36
Last Modified:	27 Feb 2026 08:36
URII:	http://shdl.mmu.edu.my/id/eprint/15373

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