Fake News Detection Using Bi-LSTM Architecture: A Deep Learning Approach on the ISOT Dataset

Maaruf M. Lawal; Abdulrashid Abdulrauf

doi:10.62411/jcta.14235

Authors

Maaruf M. Lawal Ahmadu Bello University Zaria
Abdulrashid Abdulrauf Federal Polytechnic Kaltungo

DOI:

https://doi.org/10.62411/jcta.14235

Keywords:

BiLSTM, Deep Learning, Outlier Detection, Fake News, HOAX Detection, ISOT, Misinformation Detection

Abstract

The proliferation of fake news across digital platforms has raised critical concerns about information reliability. A notable example is the viral rumour falsely claiming that the Nigerian Minister of the Federal Capital Territory, Nyesom Wike, had collapsed at an event and was rushed to an undisclosed hospital an entirely fabricated claim that caused public confusion. While both traditional machine learning and deep learning approaches have been explored for automated fake news detection, many existing models have been limited to topic-specific datasets and often suffer from overfitting, especially on smaller datasets like ISOT. This study addresses these challenges by proposing a standalone Bidirectional Long Short-Term Memory (BiLSTM) model for fake news classification using the ISOT dataset. Unlike multi-modal frameworks such as the MM-FND model by state-of-the-art model, which achieved 96.3% accuracy, the proposed BiLSTM model achieved superior results with 98.98% accuracy, 98.22% precision, 99.65% recall, and a 98.93% F1-score. The model demonstrated balanced classification across both fake and real news and exhibited strong generalization capabilities. However, training and validation performance plots revealed signs of overfitting after epoch 2, suggesting the need for regularization in future work. This study contributes to the growing body of research on fake news detection by showcasing the efficacy of a focused, sequential deep learning model over more complex architectures, offering a practical, scalable, and robust solution to misinformation detection

Author Biographies

Maaruf M. Lawal, Ahmadu Bello University Zaria

Department of Computer Science, Ahmadu Bello University Zaria, PMB 1045, Kaduna State, Nigeria

Abdulrashid Abdulrauf, Federal Polytechnic Kaltungo

Department of Computer Science, Federal Polytechnic Kaltungo, PMB 09, Gombe State, Nigeria

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