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A Unified Explainable Fake News Detection System based on Transformer-Driven Natural Language Understanding

by Mostafa Farzana, Iftee Shekh Iftesham, Tanha Asfika Jaman, Akter Mst Lovely
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 147
Year of Publication: 2026
Authors: Mostafa Farzana, Iftee Shekh Iftesham, Tanha Asfika Jaman, Akter Mst Lovely
10.5120/ijca5b7dc24854a2

Mostafa Farzana, Iftee Shekh Iftesham, Tanha Asfika Jaman, Akter Mst Lovely . A Unified Explainable Fake News Detection System based on Transformer-Driven Natural Language Understanding. International Journal of Computer Applications. 187, 147 ( Oct 2026), 39-46. DOI=10.5120/ijca5b7dc24854a2

@article{ 10.5120/ijca5b7dc24854a2,
author = { Mostafa Farzana, Iftee Shekh Iftesham, Tanha Asfika Jaman, Akter Mst Lovely },
title = { A Unified Explainable Fake News Detection System based on Transformer-Driven Natural Language Understanding },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2026 },
volume = { 187 },
number = { 147 },
month = { Oct },
year = { 2026 },
issn = { 0975-8887 },
pages = { 39-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number147/a-unified-explainable-fake-news-detection-system-based-on-transformer-driven-natural-language-understanding/ },
doi = { 10.5120/ijca5b7dc24854a2 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-10-24T03:08:39+05:30
%A Mostafa Farzana
%A Iftee Shekh Iftesham
%A Tanha Asfika Jaman
%A Akter Mst Lovely
%T A Unified Explainable Fake News Detection System based on Transformer-Driven Natural Language Understanding
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 147
%P 39-46
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online misinformation calls for classifiers that are both accurate and open to inspection. This paper presents a text-only fake news detection system that combines WordPiece tokenization, a fine-tuned BERT encoder, a linear softmax classification head, and Local Interpretable Model-Agnostic Explanations (LIME). The model is trained on a merged corpus of two public files of full-length articles, 52.3% fake and 47.7% real, whose composition is consistent with the ISOT Fake News Dataset. On a held-out test subset of 500 articles (245 fake, 255 real), the classifier makes no errors: precision, recall, F1, accuracy, the Matthews correlation coefficient, and ROC-AUC all equal 1.00. Because a perfect score on a finite sample is not proof of a zero error rate, the results are analyzed further with exact 95% confidence intervals, a zero-error sensitivity analysis, a class-proportion check of the test subset, and a comparison with results reported on other benchmark datasets. The analysis shows that, under a binomial sampling assumption, the in-domain error rate is below 0.6% with 95% confidence, that no statistically significant difference between the class proportions of the test subset and the corpus was detected (p = 0.14), and that ceiling-level scores are typical of binary article corpora rather than of harder benchmarks. LIME explanations make individual predictions inspectable and can expose source-related cues. The system is therefore a transparent baseline for explainable news-text classification, and the paper defines the cross-dataset, source-disjoint, and temporal evaluations required before deployment.

References
  1. S. Vosoughi, D. Roy, and S. Aral, “The spread of true and false news online,” Science, vol. 359, no. 6380, pp. 1146–1151, 2018.
  2. K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22–36, 2017.
  3. W. Y. Wang, “‘Liar, liar pants on fire’: A new benchmark dataset for fake news detection,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017, pp. 422–426.
  4. K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, “FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media,” Big Data, vol. 8, no. 3, pp. 171–188, 2020.
  5. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT, 2019, pp. 4171–4186.
  6. M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should I trust you?’: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
  7. R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann, “Shortcut learning in deep neural networks,” Nature Machine Intelligence, vol. 2, pp. 665–673, 2020.
  8. Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, “Hierarchical attention networks for document classification,” in Proceedings of NAACL-HLT, 2016, pp. 1480–1489.
  9. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 5998–6008.
  10. R. K. Kaliyar, A. Goswami, and P. Narang, “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach,” Multimedia Tools and Applications, vol. 80, pp. 11765–11788, 2021.
  11. S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 4765–4774.
  12. H. Ahmed, I. Traore, and S. Saad, “Detection of online fake news using n-gram analysis and machine learning techniques,” in Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments (ISDDC 2017), Lecture Notes in Computer Science, vol. 10618, Springer, 2017, pp. 127–138.
  13. H. Ahmed, I. Traore, and S. Saad, “Detecting opinion spams and fake news using text classification,” Security and Privacy, vol. 1, no. 1, e9, 2018.
  14. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Proceedings of the 34th International Conference on Machine Learning, 2017, pp. 1321–1330.
  15. M. H. Goldani, S. Momtazi, and R. Safabakhsh, “Detecting fake news with capsule neural networks,” arXiv preprint arXiv:2002.01030, 2020.
  16. I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proceedings of the International Conference on Learning Representations (ICLR), 2019.
  17. P. Micikevicius, S. Narang, J. Alben, G. Diamos, E. Elsen, D. Garcia, B. Ginsburg, M. Houston, O. Kuchaiev, G. Venkatesh, and H. Wu, “Mixed precision training,” in Proceedings of the International Conference on Learning Representations (ICLR), 2018.
  18. D. Chicco and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, vol. 21, art. 6, 2020.
  19. C. J. Clopper and E. S. Pearson, “The use of confidence or fiducial limits illustrated in the case of the binomial,” Biometrika, vol. 26, pp. 404–413, 1934.
  20. J. A. Hanley and A. Lippman-Hand, “If nothing goes wrong, is everything all right? Interpreting zero numerators,” JAMA, vol. 249, no. 13, pp. 1743–1745, 1983.
Index Terms

Computer Science
Information Sciences

Keywords

Fake news detection BERT transformer explainable artificial intelligence LIME natural language understanding

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