Detecting Fake News Using Hybrid Machine Learning Models

Abdur Rahman; Gulfam Khan; Afzal Azad; Ahmar Ejaz; Maruti Maurya

doi:10.55524/ijircst.2025.13.3.20

Abstract

The increasing diffusion of misinformation in online media has raised alarm as a significant threat to information credibility and societal trust. The ease of disseminating false information across social media platforms, news websites, and digital forums has led to severe consequences, including political manipulation, financial fraud, and public misinformation. This research outlines a robust strategy for detecting misinformation using Natural Language Processing. To take the detection process a step further, the study analyses the implementation of ensemble models. Ensemble learning combines multiple classifiers to improve generalization and robustness, reducing the likelihood of misclassification and helps the model focus on critical words and phrases that contribute to determining the authenticity of news articles. The operational efficacy of the model is measured exploiting standard evaluation assessment metric fidelity, precision, recall, and F1-score to confirm consistent performance. Inclusion of ensemble learning further improves classification accuracy by reducing biases inherent in individual models. Future work in this domain can focus on Live Monitoring for Misinformation, multilingual analysis, and incorporation of context-aware models to further refine detection capabilities. By continuously evolving NLP-based approaches, researchers and technology developers can serve an important function in mitigating the effect of misinformation on social dynamics.

Keywords

Machine Learning, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, TF-IDF vectorization, NLP (natural language Processing), Neural Network, Tokenization, stemming & lemmatization, Word vectorization Fake news detection.

References

T. Mahmud, T. Akter, M. Aziz, M. Uddin, M. Hossain, and K. Andersson, “Integration of NLP and Deep Learning for Automated Fake News Detection,” in 2024 Second International Conference on Inventive Computing and Informatics (ICICI), pp. 398–404, 2024. Available from: https://doi.org/10.1109/ICICI62254.2024.00072
G. Fang and G. Tan, “NLP in Fake News Detection,” pp. 71–83, 2021. Available from: https://doi.org/10.1007/978-981-15-9472-4_6
G. Devarajan, S. Nagarajan, S. Amanullah, S. Mary, and A. Bashir, “AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users,” IEEE Trans. Comput. Soc. Syst., vol. 11, pp. 4975–4985, 2024. Available from: https://doi.org/10.1109/TCSS.2023.3259480
M. Goldani, S. Momtazi, and R. Safabakhsh, “Detecting Fake News with Capsule Neural Networks,” ArXiv, abs/2002.01030, 2020. Available from: https://doi.org/10.1016/j.asoc.2020.106991
T. Trueman, A. J, P. Narayanasamy, and J. Vidya, “Attention-based C-BiLSTM for fake news detection,” Appl. Soft Comput., vol. 110, 107600, 2021. Available from: https://doi.org/10.1016/J.ASOC.2021.107600
R. Oshikawa, J. Qian, and W. Wang, “A Survey on Natural Language Processing for Fake News Detection,” ArXiv, abs/1811.00770, 2018. Available from: https://doi.org/10.48550/arXiv.1811.00770
V. Srikanth, “Fake News Detection System Using Machine Learning and Deep Learning,” Int. J. Sci. Res. Eng. Manag., 2024. Available from: https://doi.org/10.55041/ijsrem30766
A. Mushiba and G. Selvam, “FAKE NEWS DETECTION SYSTEM USING MACHINE LEARNING ALGORITHMS,” Int. J. Innovative Technol. Explor. Eng., vol. 8, no. 10, pp. 2019. Available from: https://doi.org/10.35940/ijitee.j9453.0881019
N. Fahad et al., “Stand up Against Bad Intended News: An Approach to Detect Fake News using Machine Learning,” Emerging Sci. J., 2023. Available from: https://doi.org/10.28991/esj-2023-07-04-015
N. Prachi, M. Habibullah, M. Rafi, E. Alam, and R. Khan, “Detection of Fake News Using Machine Learning and Natural Language Processing Algorithms,” J. Adv. Inf. Technol., vol. 13, no. 6, pp. 652–661, 2022. Available from: https://doi.org/10.12720/jait.13.6.652-661
L. M. Jupe, A. Vrij, G. Nahari, S. Leal, and S. A. Mann, “The lies we live: Using the verifiability approach to detect lying about occupation,” J. Artic. Support Null Hypoth., vol. 13, no. 1, pp. 1–13, 2016. Available from: https://tinyurl.com/3s6r58tj
Y. Li, O. Martinez, X. Chen, Y. Li, and J. E. Hopcroft, “In a world that counts: Clustering and detecting fake social engagement at scale,” in Proc. 25th Int. Conf. World Wide Web, pp. 111–120, 2016. Available from: https://doi.org/10.1145/2872427.2882972
B. P. R. Raju, B. V. Lakshmi, and C. V. L. Narayana, “Detection of Multi-Class Website URLs Using Machine Learning Algorithms,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 2, pp. 1704–1712, 2020. Available from: https://doi.org/10.30534/ijatcse/2020/122922020
R. Purohit and B. S. Borkar, “Identification of Fake vs. Real Identities on Social Media using Random Forest and Deep Convolutional Neural Network,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 7347–7351, 2019. Available from: https://doi.org/10.35940/ijeat.A9739.109119
Y. Zhang, M. J. Er, R. Venkatesan, N. Wang, and M. Pratama, “Sentiment classification using comprehensive attention recurrent models,” in 2016 Int. Jt. Conf. Neural Netw. (IJCNN), pp. 1562–1569, 2016. Available from: https://tinyurl.com/mrxfp4dn
A. Altheneyan and A. Alhadlaq, “Big Data ML-Based Fake News Detection Using Distributed Learning,” 2023. Available from: https://tinyurl.com/3sers3jx
M.-A. Ouassil, B. Cherradi, S. Hamida, M. Errami, O. El Gannour, and A. Raihani, “A Fake News Detection System based on Combination of Word Embedded Techniques and Hybrid Deep Learning Model,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 10, 2022. Available from: https://dx.doi.org/10.14569/IJACSA.2022.0131061
X. Cheng and M.-T. Kechadi, “An Enhanced Fake News Detection System With Fuzzy Deep Learning,” IEEE Access, 2024. Available from: https://doi.org/10.1109/ACCESS.2024.3418340
M. Khan and M. Haroon, “Detecting Network Intrusion in Cloud Environment Through Ensemble Learning and Feature Selection Approach,” SN Comput. Sci., vol. 5, no. 1, p. 84, 2023. Available from: https://tinyurl.com/268sh9my
W. Khan and M. Haroon, “An unsupervised deep learning ensemble model for anomaly detection in static attributed social networks,” Int. J. Cogn. Comput. Eng., vol. 3, pp. 153–160, 2022. Available from: https://doi.org/10.1016/j.ijcce.2022.08.002

Cites this article as

A. Rahman, G. Khan, A. Azad, A. Ejaz, M. Maurya, "Detecting Fake News Using Hybrid Machine Learning Models", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-13, Issue-3, Page No-127-137, 2025. Available from: https://doi.org/10.55524/ijircst.2025.13.3.20

Corresponding Author

Abdur Rahman

B. Tech Scholar, Department of Computer Science and Engineering, Integral University, Lucknow, India

Download Full Paper

Download PDF

No. of Downloads: 16 | No. of Views: 802

Spectral Geometric Regularization: Towards Isometric Invariance for Domain-Generalized Learning

Kalyan Chakravarthy Kodela, Rohith Vangalla.

September 2025 - Vol 13, Issue 5
Performance Evaluation of Ultrasonic Obstacle Detection and Real-Time GPS Tracking in an IoT Smart Shoe for Visually Impaired Navigation

Juanda Hakim Lubis, Akhyar Lubis, Fajrul Malik Aminullah Napitupulu, Nahdah Salsabiil Damanik, Lathifah Tsamratul Ain.

September 2025 - Vol 13, Issue 5
A Systematic Review of the Impact of DNS-Layer Security Mechanisms on Internet Network Performance and Threat Mitigation

Noah K. Bamfo, Christian Avornu, Abigail Nanayaa Otchill, Hassan Cessi Ibrahim, Ramini Nikhil Sai.

September 2025 - Vol 13, Issue 5

Detecting Fake News Using Hybrid Machine Learning Models

Citations

Download Full Paper PDF

Total View 802

Total Download 16