RECOGNITION OF FAKE NEWS WITH DEEP LEARNING ARCHITECTURE LSTM
DOI:
https://doi.org/10.29121/digisecforensics.v3.i1.2026.100Keywords:
Fake News Detection, Deep Learning, LSTM, Natural Language ProcessingAbstract
Fake news spread through digital channels has been a social issue due to which it has become imperative to detect such news. In this regard, this paper aims to develop a classifier for fake news based on deep learning. The proposed technique consists of various phases including preprocessing of the data to eliminate noise, tokenize data, and vectorize data after which the classifier will be trained using an LSTM model. The LSTM model suggested makes use of its potential to incorporate sequence and context-based information within the text data. The empirical findings show that the model provides 99.7% accuracy and outperforms many traditional machine learning models and contemporary hybrid models. Various other metrics such as precision, recall, and F1 score validate the effectiveness of the model. However, despite its excellent performance, more testing of the model on different datasets is recommended
References
Ajik, E. D., Obunadike, G. N., and Echobu, F. O. (2023). Fake News Detection Using Optimized CNN and LSTM Techniques. Journal of Information Systems and Informatics, 5(3), 1044–1057. https://doi.org/10.51519/journalisi.v5i3.548 DOI: https://doi.org/10.51519/journalisi.v5i3.548
Akinyemi, B. O., Adewusi, O., and Oyebade, A. (2020). An Improved Classification Model for Fake News Detection in Social Media. International Journal of Information Technology and Computer Science, 12(1), 34–43. https://doi.org/10.5815/ijitcs.2020.01.05 DOI: https://doi.org/10.5815/ijitcs.2020.01.05
Alghamdi, J., Luo, S., and Lin, Y. (2023). A Comprehensive Survey on Machine Learning Approaches for Fake News Detection. Multimedia Tools and Applications, 83(17), 51009–51067. https://doi.org/10.1007/s11042-023-17470-8 DOI: https://doi.org/10.1007/s11042-023-17470-8
D’Ulizia, A., Caschera, M. C., Ferri, F., and Grifoni, P. (2021). Fake News Detection: A Survey of Evaluation Datasets. PeerJ Computer Science, 7. https://doi.org/10.7717/peerj-cs.518 DOI: https://doi.org/10.7717/peerj-cs.518
Essa, E., Omar, K., and Alqahtani, A. (2023). Fake News Detection Based on a Hybrid BERT and LightGBM Models. Complex and Intelligent Systems, 9(6), 6581–6592. https://doi.org/10.1007/s40747-023-01098-0 DOI: https://doi.org/10.1007/s40747-023-01098-0
Güler, G., and Gunduz, S. (2023). Deep Learning Based Fake News Detection on Social Media. International Journal of Information Security Science, 12(2), 1–21. https://doi.org/10.55859/ijiss.1231423 DOI: https://doi.org/10.55859/ijiss.1231423
Haupt, M. R., Chiu, M., Chang, J., Li, Z., Cuomo, R., and Mackey, T. K. (2023). Detecting Nuance in Conspiracy Discourse: Advancing Methods in Infodemiology and Communication Science With Machine Learning and Qualitative Content Coding. PLOS ONE, 18(12). https://doi.org/10.1371/journal.pone.0295414 DOI: https://doi.org/10.1371/journal.pone.0295414
Hosseini, M., Sabet, A. J., He, S., and Aguiar, D. (2023). Interpretable Fake News Detection With Topic and Deep Variational Models. Online Social Networks and Media, 36, 100249. https://doi.org/10.1016/j.osnem.2023.100249 DOI: https://doi.org/10.1016/j.osnem.2023.100249
Keya, A. J., Shajeeb, H. H., Rahman, Md. S., and Mridha, M. F. (2023). FakeStack: Hierarchical Tri-BERT-CNN-LSTM Stacked Model for Effective Fake News Detection. PLOS ONE, 18(12). https://doi.org/10.1371/journal.pone.0294701 DOI: https://doi.org/10.1371/journal.pone.0294701
Kholiq, M. H. R., Wiranto, W., and Sihwi, S. W. (2022). News Classification Using Light Gradient Boosted Machine Algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 27(1), 206–213. https://doi.org/10.11591/ijeecs.v27.i1.pp206-213 DOI: https://doi.org/10.11591/ijeecs.v27.i1.pp206-213
Killi, C. B. R., Balakrishnan, N., and Rao, C. S. (2022). Classification of Fake News Using Deep Learning-Based GloVE-LSTM Model. International Journal of Safety and Security Engineering, 12(5), 631–637. https://doi.org/10.18280/ijsse.120512 DOI: https://doi.org/10.18280/ijsse.120512
Pierri, F., and Ceri, S. (2019). False News on Social Media. ACM SIGMOD Record, 48(2), 18–27. https://doi.org/10.1145/3377330.3377334 DOI: https://doi.org/10.1145/3377330.3377334
Rezaei, S., Kahani, M., Behkamal, B., and Jalayer, R. (2022). Early Multi-Class Ensemble Based Fake News Detection Using Content Features. Research Square. https://doi.org/10.21203/rs.3.rs-2217954/v1 DOI: https://doi.org/10.21203/rs.3.rs-2217954/v1
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., and Liu, Y. (2022). Combating Fake News: A Survey on Identification and Mitigation Techniques. arXiv. https://doi.org/10.48550/arxiv.1901.06437
Wang, Y., Wang, L., Yang, Y., and Zhang, Y. (2022). Detecting Fake News by Enhanced Text Representation With Multi-EDU-Structure Awareness. Expert Systems With Applications, 206, 117781. https://doi.org/10.1016/j.eswa.2022.117781 DOI: https://doi.org/10.1016/j.eswa.2022.117781
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Noor Hasan Fadhil
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
