The week's pick
Random Articles
Reseach Article
Adaptive Machine Learning and Deep Learning Framework for Real-time Fake News Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 55 |
| Year of Publication: 2025 |
| Authors: Vidhi Chaudhari, Ravindra Patel, Dharamsinh Solanki, Rahul Patel, Arush Aaron John |
10.5120/ijca2025925962
|
Vidhi Chaudhari, Ravindra Patel, Dharamsinh Solanki, Rahul Patel, Arush Aaron John . Adaptive Machine Learning and Deep Learning Framework for Real-time Fake News Detection. International Journal of Computer Applications. 187, 55 ( Nov 2025), 36-40. DOI=10.5120/ijca2025925962
Abstract
The rapid dissemination of fake news on digital platforms poses a critical threat to public discourse, political stability, and public health. Traditional detection methods struggle to keep pace with the evolving tactics of misinformation campaigns, prompting the need for more intelligent and adaptive systems. This research explores the application of deep learning (DL) and machine learning (ML) techniques for early and accurate detection of fake news across various domains. Through a structured review of state-of-the-art approaches—including rule-based systems, classical ML classifiers, deep learning models, and transformer-based architectures—we highlight methodological advances, dataset limitations, and system-level integration challenges. The paper also proposes a deployment-ready architecture combining real-time detection, user feedback, and robust evaluation to bridge the gap between academic research and real-world application.
References
- S. Vosoughi, D. Roy, and S. Aral, "The spread of true and false news online," Science, vol. 359, no. 6380, pp. 1146–1151, 2018. DOI: 10.1126/science.aap9559
- S. F. N. Azizah, H. D. Cahyono, S. W. Sihwi, and W. Widiarto, "Performance analysis of transformer-based models (BERT, ALBERT, and RoBERTa) in fake news detection," arXiv preprint arXiv:2308.04950, 2023. Available: https://arxiv.org/abs/2308.04950
- K. Jain, S. K. Sahu, and A. K. Singh, "Multi-modal misinformation detection: Approaches, challenges and opportunities," ACM Computing Surveys, vol. 56, no. 1, pp. 1–35, 2023. DOI: 10.1145/3697349
- V. L. Rubin, N. J. Conroy, Y. Chen, and S. Cornwell, "Fake news or truth? Using satirical cues to detect potentially misleading news," in Proc. NAACL-HLT, San Diego, CA, USA, 2016, pp. 7–17. DOI: 10.18653/v1/W16-0802
- 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. DOI: 10.1002/spy2.9
- M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, "A stylometric inquiry into hyperpartisan and fake news," arXiv preprint arXiv:1702.05638, 2017. Available: https://arxiv.org/abs/1702.05638
- W. Y. Wang, "Liar, liar pants on fire: A new benchmark dataset for fake news detection," in Proc. ACL, Vancouver, Canada, 2017, pp. 422–426. Available: https://arxiv.org/abs/1705.00648
- Y. Wang, M. McKee, and J. Torous, "Tracking and validating COVID-19 misinformation via BERT-based models," IEEE Access, vol. 9, pp. 20622–20630, 2021. DOI: 10.1109/ACCESS.2021.3059824
- K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, "FakeNewsNet: A data repository with news content, social context and dynamic information for fake news research," Big Data, vol. 8, no. 3, pp. 171–188, 2020. DOI: 10.1089/big.2020.0004
- X. Zhou and R. Zafarani, "A survey of fake news: Fundamental theories, detection methods, and opportunities," ACM Comput. Surv., vol. 53, no. 5, pp. 1–40, 2020. DOI: 10.1145/3386362
- R. K. Kaliyar, A. Goswami, and P. Narang, "FNDNet: A deep convolutional neural network for fake news detection," Neural Comput. Appl., vol. 33, pp. 13261–13276, 2021. DOI: 10.1007/s00542-020-05753-4
- S. Rajpurkar et al., "AI in the Wild: Real-Time AI for Health and News Applications," Commun. ACM, vol. 64, no. 4, pp. 58–66, 2021. DOI: 10.1145/3447781
- Full Fact, "Automated Fact Checking at Scale," Full Fact Report, 2023. Available: https://fullfact.org/
- R. Fard et al., "Adversarial examples in NLP: A survey," arXiv preprint arXiv:2101.07291, 2021. Available: https://arxiv.org/abs/2101.07291
- M. Binns, "Fairness in machine learning: Lessons from political philosophy," in Proc. 2020 AAAI/ACM Conf. AI Ethics Soc., New York, NY, USA, 2020, pp. 149–155. DOI: 10.1145/3375627.3375818
- S. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems (NeurIPS), 2017. Available: https://arxiv.org/abs/1705.07874
- Q. Yang, Y. Liu, T. Chen, and Y. Tong, "Federated machine learning: Concept and applications," ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, pp. 1–19, 2019. DOI: 10.1145/3293663
Index Terms
Keywords