International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Fake News Detection System Using Machine Learning |
|---|---|
| ABSTRACT | The rapid spread of fake news across digital media platforms has emerged as a significant societal challenge, influencing public opinion, political stability, and social harmony. Traditional rule-based and machine learning approaches often fail to capture the contextual and semantic complexity of modern news content. This paper presents a transformer-based fake news detection system that utilizes a fine-tuned DistilBERT model combined with a Django-based web application for real-time classification. The proposed system classifies news articles into REAL, FAKE, or UNSURE categories by leveraging probability-based confidence thresholds. The model is trained on multiple publicly available datasets, including Kaggle Fake News, GossipCop, Politifact, and Bharat Fake News Kosh, ensuring robustness across diverse news domains. Experimental results demonstrate a validation accuracy of 96.2%, with reliable uncertainty handling and persistent logging of predictions through a secure, user-authenticated web interface. The system offers an end-to-end, scalable, and practical solution for automated fake news detection. |
| AUTHOR | MEENAKSHI H, SYEDA AMIRA HUSSAINI, ADITHRI K S, DHANUSH SHANKAR, GOUTHAM LOKESH, INCHARA URS M Department of Computer Science and Engineering, Maharaja Institute of Technology Mysore Affiliated to Visvesvaraya Technological University (VTU), Belagavi, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401007 |
| pdf/7_Fake News Detection System Using Machine Learning.pdf | |
| KEYWORDS | |
| References | 1. T. W. Teo, M. Aziz, A. Hassan, and M. N. M. Yusof, “Integrating Large Language Models and Machine Learning for Fake News Detection,” 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), IEEE, 2024. 2. Ș. E. REPEDE and R. BRAD, “A Comparison of Artificial Intelligence Models Used for Fake News Detection,” Bulletin of “Carol I” National Defence University, vol. 12, no. 1, pp. 114–131, 2023 3. B. Jiang, L. Wang, Y. Zhang, and D. Li, “Disinformation Detection: An Evolving Challenge in the Age of LLMs,” Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), Society for Industrial and Applied Mathematics, 2024. 4. D. Boissonneault and E. Hensen, “Fake News Detection with Large Language Models on the LIAR Dataset,” unpublished manuscript, 2024. |
