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- Vol. 7 Issue 5
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Fake News Detection Using TF-IDF Feature Extraction and Supervised Machine Learning Classifiers
Authors
1 M.Tech Student, Department of Computer Engineering, M.S. Bidve Engineering College, Latur, Affiliated to Dr. Babasaheb Ambedkar Technological University (DBATU), Lonere, Raigad, India. 2 Project Guide, Department of Information Technology, M.S. Bidve Engineering College, Latur, Affiliated to Dr. Babasaheb Ambedkar Technological University (DBATU), Lonere, Raigad, India.
Article Information
- Published In
- Volume 7, Issue 5 (2026)
- Published Online
- September-October 2026
- Pages
- 107-112
- Article Type
- Original Article
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References
1. H. Allcott and M. Gentzkow, "Social media and fake news in the 2016 election," Journal of Economic Perspectives, vol. 31, no. 2, pp. 211-236, 2017.
2. 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.
3. K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, "Fake news detection on social media: A data mining perspective," ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22-36, 2017.
4. V. Perez-Rosas, B. Kleinberg, A. Lefevre, and R. Mihalcea, "Automatic detection of fake news," in Proc. 27th Int. Conf. on Computational Linguistics (COLING), Santa Fe, USA, 2018, pp. 3391-3401.
5. W. Y. Wang, "Liar, liar pants on fire: A new benchmark dataset for fake news detection," in Proc. 55th Annual Meeting of the ACL, Vancouver, Canada, 2017, pp. 422-426.
6. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proc. NAACL-HLT, Minneapolis, USA, 2019, pp. 4171-4186.
7. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
8. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. Sebastopol, CA: O'Reilly Media, 2009.
9. G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Information Processing and Management, vol. 24, no. 5, pp. 513-523, 1988.
10. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
11. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
12. N. Põldvere, Z. Uddin, and A. Thomas, “The PolitiFact-Oslo Corpus: A New Dataset for Fake News Analysis and Detection,” Information, vol. 14, no. 12, Art. no. 627, 2023, doi: 10.3390/info14120627.
2. 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.
3. K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, "Fake news detection on social media: A data mining perspective," ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22-36, 2017.
4. V. Perez-Rosas, B. Kleinberg, A. Lefevre, and R. Mihalcea, "Automatic detection of fake news," in Proc. 27th Int. Conf. on Computational Linguistics (COLING), Santa Fe, USA, 2018, pp. 3391-3401.
5. W. Y. Wang, "Liar, liar pants on fire: A new benchmark dataset for fake news detection," in Proc. 55th Annual Meeting of the ACL, Vancouver, Canada, 2017, pp. 422-426.
6. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proc. NAACL-HLT, Minneapolis, USA, 2019, pp. 4171-4186.
7. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
8. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. Sebastopol, CA: O'Reilly Media, 2009.
9. G. Salton and C. Buckley, "Term-weighting approaches in automatic text retrieval," Information Processing and Management, vol. 24, no. 5, pp. 513-523, 1988.
10. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
11. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
12. N. Põldvere, Z. Uddin, and A. Thomas, “The PolitiFact-Oslo Corpus: A New Dataset for Fake News Analysis and Detection,” Information, vol. 14, no. 12, Art. no. 627, 2023, doi: 10.3390/info14120627.
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