Abstract

The growth of social media platforms has facilitated knowledge dissemination. The ability of misinformation to affect elections, public opinion, and instigate instability makes it a dangerous threat to civilization that spreads rapidly. For an informed and reliable information ecology to survive, the ability to identify deceptive information in an extensive variety of languages is essential. The Transformer based pretrained language models (TB-PLMs) like Distilled Bidirectional Encoder Representations from Transformers (DistilBERT), ALite BERT (ALBERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) versions of the BERT model with a deep neural network structures such as Bi-directional Gated Recurrent Unit (BiGRU) and Convolution Neural Network (CNN) is used for the identification of deceptive news in English. The dataset utilized for the challenge consists of a combination of LIAR, and Fake/Real news dataset, resulting in a Combined Corpus (CC) dataset about politics. TB-PLMs are optimized to understand the semantic linkages and contextual information found in the dataset. BiGRU and CNN layers are used to capture the dependencies between neighboring characters in the text. The experimental findings show that the RoBERTa+BiGRU model performs better in comparison with all the other models in identifying English deceptive news with an accuracy of 99.04%. The results obtained reflect that RoBERTa+BiGRU has rise of 0.06% in accuracy from the base RoBERTa model. Also, the results of proposed work on DistilBERT+BiGRU and RoBERTa+BiGRU model performs well based on features (class 0 and class 1) while utilizing Local Interpretable Model-agnostic Explanations (LIME) implementation to clarify the target labels which can facilitate valid data extraction and processing to successfully counteract deceptive information.

Keywords

Deep learning, Fake News, Natural Language Processing, Political News, Transformer Based Pre-Trained Language model (TB-PLMs), XAI – LIME,

Downloads

Download data is not yet available.

References

  1. N.F. Baarir, A. Djeffal, (2021) Fake news detection using machine learning. In 2020 2nd International workshop on human-centric smart environments for health and well-being (IHSH), IEEE, Algeria. https://doi.org/10.1109/IHSH51661.2021.9378748
  2. R.M. Johnson, Social Media and Free Speech: A Collision Course That Threatens Democracy. Ohio Northern University Law Review, 49(2), (2023) 461- 487.
  3. S.Z. Akbar, A. Panda, J. Pal, (2024) Political hazard: Misinformation in the 2019 Indian general election campaign. In Political Campaigning in Digital India, Routledge.
  4. W. Y. Wang, LIAR, LIAR pants on fire. A new benchmark dataset for fake news detection. arXiv preprint arXiv, 1705.00648. https://doi.org/10.48550/arXiv.1705.00648
  5. A. Hande, K. Puranik, R. Priyadharshini, S. Thavareesan, B.R. Chakravarthi, (2021). Evaluating pretrained transformer-based models for COVID-19 fake news detection. In 2021 5th international conference on computing methodologies and communication (ICCMC), IEEE, India. https://doi.org/10.1109/ICCMC51019.2021.9418446
  6. S. Raza, C. Ding, Fake news detection based on news content and social contexts: a transformer-based approach. International Journal of Data Science and Analytics, 13(4), (2022) 335-362. https://doi.org/10.1007/s41060-021-00302-z
  7. R. Mohawesh, S. Maqsood, Q. Althebyan, Multilingual deep learning framework for fake news detection using capsule neural network. Journal of Intelligent Information Systems, 60(3), (2023) 655-671. https://doi.org/10.1007/s10844-023-00788-y
  8. S. Kula, M. Choras, R. Kozik, (2021). Application of the BERT-based architecture in fake news detection. In 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020), CISIS 2019. Advances in Intelligent Systems and Computing, Springer, Cham. https://doi.org/10.1007/978-3-030-57805-3_23
  9. H. Jwa, D. Oh, K. Park, J.M. Kang, H. Lim, exbake: Automatic fake news detection model based on bidirectional encoder representations from transformers (BERT). Applied Sciences, 9(19), (2019) 4062. https://doi.org/10.3390/app9194062
  10. F. Al-Quayed, D. Javed, N.Z. Jhanjhi, M. Humayun, T.S. Alnusairi, Optimizing Fake News Detection. A Hybrid Transformer-Based Model for Enhanced Performance, IEEE Access, 12, (2024) 160822 – 160834. https://doi.org/10.1109/ACCESS.2024.3476432
  11. P. Gupta, S. Gandhi, B.R. Chakravarthi, Leveraging transfer learning techniques-bert, roberta, albert and distilbert for fake review detection. In Proceedings of the 13th Annual Meeting of the Forum for Information Retrieval Evaluation, (2021) 75-82. https://doi.org/10.1145/3503162.3503169
  12. S.R. Sahoo, B.B. Gupta, Multiple features-based approach for automatic fake news detection on social networks using deep learning. Applied Soft Computing, 100, (2021) 106983. https://doi.org/10.1016/j.asoc.2020.106983
  13. S. Hakak, M. Alazab, S. Khan, T.R. Gadekallu, P.K.R. Maddikunta, W.Z. Khan, An ensemble machine learning approach through effective feature extraction to classify fake news. Future Generation Computer Systems, 117, (2021) 47-58. https://doi.org/10.1016/j.future.2020.11.022
  14. A. Jarrahi, L. Safari, (2023). Evaluating the effectiveness of publishers’ features in fake news detection on social media. Multimedia Tools and Applications, 82(2), (2023) 2913-2939. https://doi.org/10.1007/s11042-022-12668-8
  15. T. Pavlov, G. Mirceva, (2022), May Covid-19 fake news detection by using bert and roberta models. 45th Jubilee International Convention on Information. Communication and Electronic Technology (MIPRO), IEEE, Croatia. https://doi.org/10.23919/MIPRO55190.2022.9803414
  16. C. Busioc, V. Dumitru, S. Ruseti, S. Terian-Dan, M. Dascalu, T. Rebedea, (2022). What are the latest fake news in romanian politics? an automated analysis based on bert language models. In Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Proceedings of the 6th International Conference on Smart Learning Ecosystems and Regional Development, Springer Singapore. https://doi.org/10.1007/978-981-16-3930-2_16
  17. A. Wani, I. Joshi, S. Khandve, V. Wagh, R. Joshi, (2021). Evaluating deep learning approaches for covid19 fake news detection. In International Workshop on Combating On line Ho stile Posts in Regional Languages during Emergency Situition Springer International Publishing, Springer, Cham. https://doi.org/10.1007/978-3-030-73696-5_15
  18. J. Alghamdi, Y. Lin, S. Luo, (2023). Towards COVID-19 fake news detection using transformer-based models. Knowledge-Based Systems, 274, (2023) 110642. https://doi.org/10.1016/j.knosys.2023.110642
  19. J.Y. Khan, Md.T.I. Khondaker, S. Afroz, G. Uddin, A. Iqbal, A benchmark study of machine learning models for online fake news detection. Machine Learning with Applications, 4, (2021) 100032. https://doi.org/10.1016/j.mlwa.2021.100032
  20. M. Aman, Large language model based fake news detection. Procedia Computer Science, 231, (2024) 740-745. https://doi.org/10.1016/j.procs.2023.12.144
  21. B.Chen, B. Chen, D. Gao, Q. Chen, C. Huo, X. Meng, W. Ren, Y. Zhou,(2021).Transformer-based language model fine-tuning methods for COVID-19 fake news detection. In International Workshop onCombating on line Ho st ile Posts in Regional Languages during Emergency Situition. Springer, Cham. https://doi.org/10.1007/978-3-030-73696-5_9
  22. S. Kula, R. Kozik, M. Choras, M. Woźniak, (2021). Transformer based models in fake news detection. Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, Springer, Cham.
  23. M. Samadi, M. Mousavian, S. Momtazi, Deep contextualized text representation and learning for fake news detection. Information processing & management, 58(6), (2021) 102723. https://doi.org/10.1016/j.ipm.2021.102723
  24. O. Bashaddadh, N. Omar, M. Mohd, M.N. Akmal Khalid, Machine Learning and Deep Learning Approaches for Fake News Detection. A Systematic Review of Techniques, Challenges, and Advancements, IEEE Access, 13, (2025) 90433 – 90466. https://doi.org/10.1109/ACCESS.2025.3572051
  25. Bo Hu, Z. Mao, Y. Zhang, An overview of fake news detection; from a new perspective. Fundamental Research, 5(1), (2025) 332-346. https://doi.org/10.1016/j.fmre.2024.01.017
  26. K. Irfan, M. Wasim, S. Safdar, A. Rehman, M.U. Ghani, (2025). XFND: Explainable Fake News Detection using a Hybrid DistillBERT and BiLSTM. In 2025 International Conference on Emerging Technologies in Electronics, Computing, and Communication (ICETECC), IEEE, Pakistan. https://doi.org/10.1109/ICETECC65365.2025.11070272
  27. M. Al-alshaqi, D.B. Rawat, C. Liu, A BERT-Based Multimodal Framework for Enhanced Fake News Detection Using Text and Image Data Fusion. Computers, 14(6), (2025) 237. https://doi.org/10.3390/computers14060237
  28. A. De, D. Bandyopadhyay, B. Gain, A. Ekbal, A transformer-based approach to multilingual fake news detection in low-resource languages. Transactions on Asian and Low-Resource Language Information Processing, 21(1), (2021) 1-20. https://doi.org/10.1145/3472619
  29. E. Essa, K. Omar, A, Alqahtani, Fake news detection based on a hybrid BERT and LightGBM models. Complex & Intelligent Systems, 9(6), (2023) 6581-6592. https://doi.org/10.1007/s40747-023-01098-0
  30. S. Kula, R. Kozik, M. Choras, Implementation of the BERT-derived architectures to tackle disinformation challenges. Neural Computing and Applications, 34(23), (2022) 20449-20461. https://doi.org/10.1007/s00521-021-06276-0
  31. S. Nwaiwu, N. Jongsawat, A. Tungkasthan, A. Decoding Disinformation: A Feature-Driven Explainable AI Approach to Multi-Domain Fake News Detection. Applied Sciences, 15(17), (2025) 9498. https://doi.org/10.3390/app15179498
  32. Saadi, Abdelhalim, H. Belhadef, A. Guessas, O. Hafirassou, Enhancing Fake News Detection with Transformer Models and Summarization. Engineering, Technology & Applied Science Research, 15(3), (2025) 23253-23259. https://doi.org/10.48084/etasr.10678
  33. A. Praseed, J. Rodrigues, P.S. Thilagam, Hindi fake news detection using transformer ensembles. Engineering Applications of Artificial Intelligence, 119, (2023) 105731. https://doi.org/10.1016/j.engappai.2022.105731
  34. H.R. LekshmiAmmal, A.K. Madasamy, A reasoning based explainable multimodal fake news detection for low resource language using large language models and transformers. Journal of Big Data, 12(1), (2025) 46. https://doi.org/10.1186/s40537-025-01093-x
  35. R. Jadhav, V. Meshram, A. Bhosle, K. Patil, S. Dash, S. Jadhav, Explainable Multilingual and Multimodal Fake News Detection: Towards Robust and Trustworthy AI for Combating Misinformation. Frontiers in Artificial Intelligence, 8, (2025) 1690616.
  36. X. Men V.Y. Mariano, Explainable Fake News Detection Based on BERT and SHAP Applied to COVID-19. International Journal of Modern Education and Computer Science (IJMECS), 16(1), (2024) 11-22.
  37. M. Samadi, S. Momtazi, Fake news detection: deep semantic representation with enhanced feature engineering. International Journal of Data Science and Analytics, 20(2), (2025) 325-336. https://doi.org/10.1007/s41060-023-00387-8
  38. V. Sanh, L. Debut, J. Chaumond, T. Wolf, (2019) DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv 1910.01108. https://doi.org/10.48550/arXiv.1910.01108
  39. S. Silalahi, T. Ahmad, H. Studiawan, (2022). Named entity recognition for drone forensic using Bert and DistilBERT. In 2022 International Conference on Data Science and Its Applications (ICoDSA), IEEE, Indonesia. https://doi.org/10.1109/ICoDSA55874.2022.9862916
  40. S.F.N Azizah, H.D. Cahyono, S.W. Sihwi, W. Widiarto, (2023). Performance analysis of transformer based models (BERT, ALBERT, and RoBERTa) in fake news detection. In 2023 6th International Conference on Information and Communications Technology (ICOIACT), IEEE, Indonesia. https://doi.org/10.1109/ICOIACT59844.2023.10455849
  41. H. ELFAIK, Automatic detection of fake news using gated recurrent unit deep model. Procedia Computer Science, 233, (2024) 474-480. https://doi.org/10.1016/j.procs.2024.03.237
  42. H. Saleh, A. Alharbi, S.H. Alsamhi, OPCNN-FAKE: Optimized convolutional neural network for fake news detection. IEEE Access, 9, (2021) 129471-129489. https://doi.org/10.1109/ACCESS.2021.3112806
  43. Y. Wang, Y. Zhang, X. Li, X. Yu, (2021) Covid-19 fake news detection using bidirectional encoder representations from transformers based models. arXiv preprint arXiv:2109.14816. https://doi.org/10.48550/arXiv.2109.14816
  44. T. Shwetha, R. Buvanaa, J. Jayabharathy, I. Sivasakthi, R.H. Sai, Fake News Detection Using Hybrid Transformer-Based Model. IJSAT-International Journal on Science and Technology, 16(2), (2025) https://doi.org/10.71097/IJSAT.v16.i2.5305
  45. Leveraging Bayesian optimization and bidirectional recurrent unit. arXiv preprint arXiv:2502.09097. https://doi.org/10.48550/arXiv.2502.09097
  46. M.I. Nadeem, S.A.H. Mohsan, K. Ahmed, D. Li, Z. Zheng, M. Shafiq, S.M. Mostafa, HyproBert: A fake news detection model based on deep hypercontext. Symmetry, 15(2), (2023) 296. https://doi.org/10.3390/sym15020296
  47. G. Joshi, A. Srivastava, B. Yagnik, M. Hasan, Z. Saiyed, L.A. Gabralla, K. Kotecha, Explainable misinformation detection across multiple social media platforms. IEEE Access, 11, (2023) 23634-23646. https://doi.org/10.1109/ACCESS.2023.3251892
  48. V. Dua, A. Rajpal, S. Rajpal, M. Agarwal, N. Kumar, I-flash: Interpretable fake news detector using lime and shap. Wireless Personal Communications, 131(4), (2023) 2841-2874. https://doi.org/10.1007/s11277-023-10582-2
  49. A.U. Hussna, I.I. Trisha, M.S. Karim, M.G.R. Alam, (2021) COVID-19 fake news prediction on social media data. IEEE Region 10 Symposium (TENSYMP), IEEE, Korea. https://doi.org/10.1109/TENSYMP52854.2021.9550957
  50. K. Pelrine, J. Danovitch, R. Rabbany, The surprising performance of simple baselines for misinformation detection. In Proceedings of the web conference, (2021) 3432-3441. https://doi.org/10.1145/3442381.3450111
  51. S. Malla, P.J.A Alphonse, Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news. The European Physical Journal Special Topics, 231(18), (2022) 3347-3356. https://doi.org/10.1140/epjs/s11734-022-00436-6