A Deep Learning Approach to Fake News Classification Using LSTM
DOI:
https://doi.org/10.26740/vubeta.v2i3.39360Keywords:
Deep Learning, Fake News Classification, LSTM, NLPAbstract
The rapid spread of misinformation on digital platforms poses a major challenge today. The ability to detect false information is essential to mitigate the associated harmful consequences. This research presents a deep learning approach for detecting fake news using Long Short-Term Memory (LSTM) model, which captures linguistic patterns and long-term dependencies in text. Our approach consists of optimizing the model through different experiments based on hyperparameter tuning, on a pre-processed dataset. The evaluation is performed using different metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the LSTM model achieves high accuracy of 0.9974, with embedding dimension of 128 using 100 LSTM units, batch size of 64 and drop-out rate of 0.48. It is a substantial improvement over previous studies. The application of cross-validation further confirms the model’s reliability. This research demonstrates that the application of a fine-tuned LSTM network with robust data preprocessing can provide a powerful tool to combat online misinformation.
Author Biographies
Sitraka Herinambinina Andrianarisoa, School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius
Sitraka Herinambinina Andrianarisoa has completed a Master's degree in Artificial Intelligence with Machine Learning from School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius. He has worked on several machine learning projects, with a focus on deep learning. He can be contacted at sitraka.andrianarisoa@umail.utm.ac.mu
Henri Michaël Ravelonjara, School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius
Henri Michaël Ravelonjara has completed a Master's degree in Artificial Intelligence with Machine Learning from School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius. He has worked on several machine learning projects, with a focus on deep learning. He can be contacted at henri.ravelonjara@umail.utm.ac.mu
Geerish Suddul, Department of Business Informatics and Software Engineering, School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius
Geerish Suddul received his Ph.D. from the University of Technology, Mauritius (UTM). He is currently a Senior Lecturer at the UTM, in the Department of Business Informatics and Software Engineering under the School of Innovative Technologies and Engineering. He has been actively involved in research and teaching since 2005, and currently his research work focuses on different aspects of machine learning such as computer vision and natural language processing. He can be contacted at g.suddul@utm.ac.mu.
Ravi Foogooa, Department of Business Informatics and Software Engineering, School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius
Ravi Foogooa received his PhD from the University of Technology, Mauritius (UTM). He is currently a Senior Lecturer, in the Department of Business Informatics and Software Engineering under the School of Innovative Technologies and Engineering. He has worked as an IT professional for eight years before joining academia in 2002. His research interests include outsourcing of information systems, software project management and sustainable ICT. He can be contacted at rfoogooa@utm.ac.mu
Sandhya Armoogum, Department of Industrial Systems Engineering, School of Innovative Technologies and Engineering, University of Technology La Tour Koenig, Pointe-aux-Sables, Republic of Mauritius
Sandhya Armoogum received her Ph.D. from the University of Technology, Mauritius (UTM). She is currently an Associate Professor at the UTM, in the Department of Industrial Systems Engineering under the School of Innovative Technologies and Engineering. She has been actively involved in research and teaching since 2003, and currently her research work focuses on different aspects of machine learning, cybersecurity and big data analytics. She can be contacted at sandhya.armoogum@utm.ac.mu.
Doorgesh Sookarah, Department of Industrial Systems Engineering, School of Innovative Technologies and Engineering, University of Technology, Mauritius
Doorgesh Sookarah received his MSc in Artificial Intelligence from the University of Essex, UK. He is currently a Lecturer at the University of Technology, Mauritius (UTM), in the Department of Industrial Systems Engineering under the School of Innovative Technologies and Engineering. He has been actively involved in teaching, programme development, and applied research since 2015. His research interests include applicative and generative AI, digital transformation, and the integration of emerging technologies. He can be contacted at doorgesh.sookarah@utm.ac.mu.
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Copyright (c) 2025 Sitraka Herinambinina Andrianarisoa, Henri Michaël Ravelonjara, Geerish Suddul, Ravi Foogooa, Sandhya Armoogum
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