Authors : Aliyu Ibrahim Sulaiman; Dr. Fakhrun Jamal; Abdurrazaq Jibril Baba; Yahaya Salisu; Mukhtar Yunusa Maijamaa; Dr. Yusuf Aliyu Adam

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/bdedphj2

Scribd : https://tinyurl.com/utrx7nzd

DOI : https://doi.org/10.38124/ijisrt/25dec547

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : As the use of digital platforms increases, the proliferation of fake news plays a pivotal role in influencing trust in their use, both ethically and socially, as well as politically. Traditional fake news detection system focuses on Machine Learning (ML), Natural Language Processing (NLP), and Deep Learning (DL), which naturally rely on textual analysis. The review emphasizes an extensive approach to incorporating contextual cues and emotional influences on behavior, which influence the validity of the information. The research examined the existing literature and identified gaps within the model's architecture, including a lack of a proper emotional dataset, inadequate support for multiple languages, insufficient implementation of ethics in behavior training for user behavior, and limited real-time detection proficiency. The study suggests integrating explainable AI (XAI) for transparency in model prediction, multi-cultural emotion modeling, and differential privacy to protect users' data privacy, as well as addressing challenges in adopting the technology. The paper highlight the importance adoption of hybrid modelling, which also boosts the accuracy of the detection system. The goal is to contribute to the development of a transparent, effective, and robust model for detecting fake news, supporting multi- cultural and diverse linguistic contexts. The review contributes to advancing research on creating more computational fact- checking systems by integrating emotional and contextual cues as a way forward in alleviating fake news in the digital era.

Keywords : Fake News; Contextual Analysis; Emotional Cues; Machine Learning; Natural Language Processing; Explainable AI.

References :

1. Council of Europe, “Dealing with propaganda, misinformation and fake news - Democratic Schools for All,” [Online]. Available: https://www.coe.int/en/web/campaign-free-to-speak-safe-to-learn/dealing-with-propaganda-misinformation-and-fake-news. Accessed: May 29, 2025.
2. European Union, “data.europa.eu,” [Online]. Available: https://data.europa.eu/data/datasets/s2183_464_eng?locale=en. Accessed: May 30, 2025.
3. BBC News, “A year in fake news in Africa,” [Online]. Available: https://www.bbc.com/news/world-africa-46127868. Accessed: May 30, 2025.
4. M. A. B. Al-Tarawneh, O. Al-irr, K. S. Al-Maaitah, H. Kanj, and W. H. F. Aly, “Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach,” Computers, vol. 13, no. 9, p. 239, Sept. 2024.
5. T. S. Camelia, F. R. Fahim, and M. M. Anwar, “A Regularized LSTM Method for Detecting Fake News Articles,” arXiv preprint arXiv:2411.10713, 2024.
6. A. A. Olagunju and I. O. Awoyelu, “Performance Evaluation of Fake News Detection Models,” Int. J. Inf. Technol. Comput. Sci., vol. 16, no. 6, pp. 89–100, Dec. 2024.
7. M. V. Rampurkar, “An Approach towards Fake News Detection using Machine Learning Techniques,” unpublished.
8. J. Alghamdi, S. Luo, and Y. Lin, “A comprehensive survey on machine learning approaches for fake news detection,” Multimed. Tools Appl., vol. 83, no. 17, pp. 51009–51067, Nov. 2023.
9. F. Folino, G. Folino, M. Guarascio, L. Pontieri, and P. Zicari, “Towards Data- and Compute-Efficient Fake-News Detection: An Approach Combining Active Learning and Pre-Trained Language Models,” SN Comput. Sci., vol. 5, no. 5, p. 470, Apr. 2024.
10. M. Bozuyla and A. Özçift, “Developing a fake news identification model with advanced deep learning,” Turk. J. Electr. Eng. Comput. Sci., 2024.
11. K. Machová, M. Mach, and V. Balara, “Federated Learning in the Detection of Fake News Using Deep Learning as a Basic Method,” Sensors, vol. 24, no. 11, p. 3590, Jun. 2024.
12. S. Harris, H. J. Hadi, N. Ahmad, and M. A. Alshara, “Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas,” Technologies, vol. 12, no. 11, p. 222, Nov. 2024.
13. M. Haqi Al-Tai, B. M. Nema, and A. Al-Sherbaz, “Deep Learning for Fake News Detection: Literature Review,” Al-Mustansiriyah J. Sci., vol. 34, no. 2, pp. 70–81, Jun. 2023.
14. C.-O. Truică and E.-S. Apostol, “It’s All in the Embedding! Fake News Detection Using Document Embeddings,” Mathematics, vol. 11, no. 3, p. 508, Jan. 2023.
15. S. Singhania, N. Fernandez, and S. Rao, “3HAN: A Deep Neural Network for Fake News Detection,” in Proc. Int. Conf., 2017, pp. 572–581.
16. M. H. Goldani, R. Safabakhsh, and S. Momtazi, “X-CapsNet For Fake News Detection,” arXiv preprint arXiv:2307.12332, Jul. 2023.
17. D. P. Malik, R. Pandit, A. Chourasia, D. L. Singh, D. P. Rane, and D. Piyush, “Automated Fake News Detection: Approaches, Challenges, and Future Directions,” unpublished.
18. S. Kh. Hamed, M. J. Ab Aziz, and M. R. Yaakub, “Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments,” Sensors, vol. 23, no. 4, p. 1748, Feb. 2023.
19. T. Majeed, H. Farooq, T. Jan, and A. Nazir, “Fake News Detection: A Review,” in Proc. 3rd Int. Conf. ICT for Digital, Smart, and Sustainable Development (ICIDSSD), New Delhi, India, Mar. 2022.
20. N. Vo and K. Lee, “Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection,” arXiv preprint arXiv:2102.02680, Feb. 2021.
21. S. D. Das, A. Basak, and S. Dutta, “A Heuristic-driven Uncertainty based Ensemble Framework for Fake News Detection in Tweets and News Articles,” arXiv preprint arXiv:2104.01791, Dec. 2021.
22. A. Choudhary and A. Arora, “Linguistic feature based learning model for fake news detection and classification,” Expert Syst. Appl., vol. 169, p. 114171, May 2021.
23. S. Sari and U. Bayram, “Vaccine hesitancy in Türkiye: A natural language processing study on social media,” Turk. J. Electr. Eng. Comput. Sci., 2023.
24. A. M. Sanadi and V. Bhat, “A Review Paper on Natural Language Processing (NLP),” Int. J. Adv. Res. Sci. Commun. Technol., pp. 23–29, Mar. 2022.
25. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
26. 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,” in Proc. 6th Int. Conf. on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, Nov. 2023, pp. 425–430.
27. N. Hoy and T. Koulouri, “An exploration of features to improve the generalisability of fake news detection models,” Expert Syst. Appl., vol. 275, p. 126949, May 2025.
28. S. Bansal, N. S. Singh, S. S. Dar, and N. Kumar, “MMCFND: Multimodal Multilingual Caption-aware Fake News Detection for Low-resource Indic Languages,” arXiv preprint arXiv:2410.10407, Oct. 2024.
29. L. Allein, M.-F. Moens, and D. Perrotta, “Preventing profiling for ethical fake news detection,” Inf. Process. Manag., vol. 60, no. 2, p. 103206, Mar. 2023.
30. N. Cavus, M. Goksu, and B. Oktekin, “Real-time fake news detection in online social networks: FANDC Cloud-based system,” Sci. Rep., vol. 14, no. 1, p. 25954, Oct. 2024.
31. M. Liu et al., “MisD-MoE: A Multimodal Misinformation Detection Framework with Adaptive Feature Selection,” SSRN preprint, 2024.
32. S. S. Debata and S. Elango, “A Multimodal Attention-Driven Framework for Fake News Detection,” SSRN preprint, 2025.
33. Z. Liu, T. Zhang, K. Yang, P. Thompson, Z. Yu, and S. Ananiadou, “Emotion Detection for Misinformation: A Review,” Inf. Fusion, vol. 107, p. 102300, Jul. 2024.
34. J. M. Stella, K. Akhila, and B. N. Nishitha, “Real-Time Deepfake Detection and Emotion Analysis from Tweets, Images and Videos,” Int. J. Eng. Res., vol. 14, no. 06, 2025.
35. F. Jamal, “Emerging Technologies and Developments in Cloud Computing: A Systematic Review,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 3, pp. 894–905, Mar. 2020.