A mathematical framework for fake news detection in digital media using an intelligent fuzzy neural network model
Yash Vardhan Singhyash3005vardhan@gmail.comDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , *Prashant VatsCorresponding authorprashant.vats@jaipur.manipal.eduDepartment of Computer Science & EngineeringManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 31 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2334
- Pages:
- 3083–3092
Abstract
The explosive growth of digital platforms has intensified the circulation of false information, undermining public confidence and societal harmony. Conventional machine learning and deep learning methods often lack transparency and struggle with vague or noisy text. This study introduces a Fuzzy Neural Network that merges neural adaptability with fuzzy logic’s ability to manage uncertainty. Linguistic, semantic, and credibility features are modeled through fuzzy membership functions and refined via neural learning. Evaluation on standard datasets shows improved accuracy and clear, interpretable decisions, supporting dependable real-world misinformation detection.
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References
[1] R. Kumar, A. Suresh, and V. Natarajan, “Adaptive fuzzy inference model for uncertainty-aware image classification,” AIP Conf. Proc., (2024), doi: 10.1063/5.0217284.
[2] S. Karthikeyan, M. Selvakumar, and P. Arunachalam, “Fuzzy-assisted edge intelligence for IoT data analytics,” in Proc. IEEE Int. Conf. Intell. Innov. Comput. Syst. (I-PACT), (2023), doi: 10.1109/I-PACT58649.2023.
[3] P. Nagarajan, R. Prasath, and B. Kumar, “Hybrid fuzzy-attention model for context-aware sentiment and intent classification,” in Proc. IEEE Conf. Comput. Intell. Inf. Syst. (ICIICS), (2023), doi: 10.1109/ICIICS59993.2023.10421322.
[4] A. Kumar and R. Yadav, “Fake news detection using NLP and semantic features,” Data Knowl. Eng., vol. 147, Art. no. 102165 (2024).
[5] A. M. Hasan, N. K. N. Al-Waely, H. K. Aljobouri, H. A. Jalab, R. W. Ibrahim, and F. Meziane, “Corrigendum to ‘Molecular subtypes classification of breast cancer in DCE-MRI using deep features’ [Expert Syst. Appl. 236 (2024) 121371],” Expert Syst. Appl., vol. 270, Art. no. 126763 (2025), doi: 10.1016/j.eswa.2025.126763.
[6] D. Johnson and L. Wang, “Social graph signals in fake news detection,” Soc. Netw. Anal. Min., vol. 14, no. 2 (2024).
[7] R. Bharati, J. Bharti, and V. Dehalwar, “A novel fuzzy logic-based hybrid framework for detecting fake news,” Discover Comput., vol. 29, no. 1, pp. 1–28 (2026), doi: 10.1007/s10791-025-09882-x.
[8] C. Xu and M.-T. Kechadi, “Fuzzy deep hybrid network for fake news detection,” in Proc. 12th Int. Symp. Inf. Commun. Technol. (SOICT), pp. 118–125 (2023), doi: 10.1145/3628797.3628971.
[9] T. M. H. Gedara, V. Loia, and S. Tomasiello, “A fuzzy-based multimodal approach for interpretable fake news detection,” Appl. Soft Comput., vol. 179, Art. no. 113277 (2025), doi: 10.1016/j.asoc.2025.113277.
[10] B. Mohamed, H. Hmami, and A. Fennan, “Applying fuzzy logic and neural network in sentiment analysis for fake news detection: Case of COVID-19,” in Combating Fake News With Computational Intelligence Techniques, pp. 387–400 (2021).
[11] Y. Lu and N. Yao, “A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network,” Sci. Rep., vol. 15, Art. no. 20544 (2025), doi: 10.1038/s41598-025-05702-w.
[12] S. B. Shunmugavel, S. K. Nagarajan, K. D. Rangaswamy, and M. Muthiah, “A hybrid framework for fake news detection using explainability of artificial intelligence,” Recent Pat. Eng., vol. 19, no. 9, Art. no. E18722121313149 (2025), doi: 10.2174/0118722121313149240729044309.
[13] C. Mallick, S. Mishra, S. Satpathy, and M. M. V. Chalapathi, “A gated recurrent unit-based model for fake news detection in online social networks,” J. Inf. Optim. Sci., vol. 46, no. 6, pp. 1841–1850 (2025), doi: 10.47974/JIOS-2013.
[14] S. Kalra, Y. Bansal, Y. Sharma, and G. S. Chauhan, “FakeSpotter: A blockchain-based trustworthy idea for fake news detection in social media,” J. Inf. Optim. Sci., vol. 44, no. 3, pp. 515–527 (2023), doi: 10.47974/JIOS-1411.
[15] Z. A. Jawad and A. J. Obaid, “A systemic literature overview of Fake News Challenge (FNC-1) dataset and its use in fake news detection schemes,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 4, pp. 1197–1206 (2023), doi: 10.47974/JDMSC-1567.
[16] S. Kalra, C. H. S. Kumar, Y. Sharma, and G. S. Chauhan, “FakeExpose: Uncovering the falsity of news by targeting multimodality via transfer learning,” J. Inf. Optim. Sci., vol. 44, no. 3, pp. 301–314 (2023), doi: 10.47974/JIOS-1342.




