Fake News Detection Using Machine Learning: A Comparative Study of Naive Bayes, Logistic Regression, and Linear Support Vector Machine with TF-IDF Features

The rapid growth of digital misinformation has created an urgent need for computational tools that can identify misleading news content at scale. This paper presents a comparative study of three supervised machine-learning classifiers, Multinomial Naive Bayes, Logistic Regression, and Linear Support Vector Machine (LinearSVC), for binary fake-news classification using TF-IDF text features. The experimental analysis reports values available from the single-split benchmark and dataset description. The cleaned dataset contains 44,898 articles, including 23,481 fake-news articles and 21,417 real-news articles. In the reported 80:20 split, LinearSVC achieves the strongest performance with 99.3% accuracy and approximately 0.99 precision, recall, and F1-score, followed by Logistic Regression at 98.7% accuracy and Multinomial Naive Bayes at 88.5% accuracy. Because very high accuracy on a single dataset may be influenced by dataset-specific lexical or source patterns, the paper discusses reproducibility, explainability, dataset bias, and future external validation requirements before real-world deployment.

Fake news detection, machine learning, natural language processing, TF-IDF, Naive Bayes, Logistic Regression, LinearSVC, misinformation.

1. [1] 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. [Google Scholar] [Crossref]

2. [2] D. M. J. Lazer et al., “The science of fake news,” Science, vol. 359, no. 6380, pp. 1094–1096, 2018, doi: 10.1126/science.aao2998. [Google Scholar] [Crossref]

3. [3] H. Allcott and M. Gentzkow, “Social media and fake news in the 2016 election,” J. Econ. Perspect., vol. 31, no. 2, pp. 211–236, 2017, doi: 10.1257/jep.31.2.211. [Google Scholar] [Crossref]

4. [4] World Health Organization, “Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation,” 2020. [Online]. Available: https://www.who.int/news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation [Google Scholar] [Crossref]

5. [5] L. Graves, “Understanding the promise and limits of automated fact-checking,” Reuters Institute for the Study of Journalism, 2018. [Online]. Available: https://reutersinstitute.politics.ox.ac.uk/our-research/understanding-promise-and-limits-automated-fact-checking [Google Scholar] [Crossref]

6. [6] N. J. Conroy, V. L. Rubin, and Y. Chen, “Automatic deception detection: Methods for finding fake news,” Proc. Assoc. Inf. Sci. Technol., vol. 52, no. 1, pp. 1–4, 2015, doi: 10.1002/pra2.2015.145052010082. [Google Scholar] [Crossref]

7. [7] C. Castillo, M. Mendoza, and B. Poblete, “Information credibility on Twitter,” in Proc. 20th Int. Conf. World Wide Web, 2011, pp. 675–684, doi: 10.1145/1963405.1963500. [Google Scholar] [Crossref]

8. [8] H. Ahmed, I. Traore, and S. Saad, “Detection of online fake news using n-gram analysis and machine learning techniques,” in Proc. Int. Conf. Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, 2017, pp. 127–138, doi: 10.1007/978-3-319-69155-8_9. [Google Scholar] [Crossref]

9. [9] B. D. Horne and S. Adali, “This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news,” in Proc. ICWSM Workshop, 2017. [Online]. Available: https://ojs.aaai.org/index.php/ICWSM/article/view/14976 [Google Scholar] [Crossref]

10. [10] M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, “A stylometric inquiry into hyperpartisan and fake news,” in Proc. 56th Annu. Meeting ACL, 2018, pp. 231–240, doi: 10.18653/v1/P18-1022. [Google Scholar] [Crossref]

11. [11] G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manage., vol. 24, no. 5, pp. 513–523, 1988, doi: 10.1016/0306-4573(88)90021-0. [Google Scholar] [Crossref]

12. [12] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. Sebastopol, CA, USA: O’Reilly Media, 2009. [Online]. Available: https://www.nltk.org/book/ [Google Scholar] [Crossref]

13. [13] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. [Online]. Available: https://jmlr.org/papers/v12/pedregosa11a.html [Google Scholar] [Crossref]

14. [14] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735. [Google Scholar] [Crossref]

15. [15] W. Y. Wang, “Liar, liar pants on fire: A new benchmark dataset for fake news detection,” in Proc. 55th Annu. Meeting ACL, 2017, pp. 422–426, doi: 10.18653/v1/P17-2067. [Google Scholar] [Crossref]

16. [16] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186, doi: 10.18653/v1/N19-1423. [Google Scholar] [Crossref]

17. [17] A. Vaswani et al., “Attention is all you need,” in Proc. NeurIPS, 2017, pp. 5998–6008. [Online]. Available: https://papers.nips.cc/paper/7181-attention-is-all-you-need [Google Scholar] [Crossref]

18. [18] R. K. Kaliyar, A. Goswami, P. Narang, and S. Sinha, “FakeBERT: Fake news detection in social media with a BERT-based deep learning approach,” Multimedia Tools Appl., vol. 80, no. 8, pp. 11765–11788, 2021, doi: 10.1007/s11042-020-10183-2. [Google Scholar] [Crossref]

19. [19] Y. Liu et al., “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv:1907.11692, 2019. [Online]. Available: https://arxiv.org/abs/1907.11692 [Google Scholar] [Crossref]

20. [20] V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,” arXiv:1910.01108, 2019. [Online]. Available: https://arxiv.org/abs/1910.01108 [Google Scholar] [Crossref]

21. [21] N. Ruchansky, S. Seo, and Y. Liu, “CSI: A hybrid deep model for fake news detection,” in Proc. ACM CIKM, 2017, pp. 797–806, doi: 10.1145/3132847.3132877. [Google Scholar] [Crossref]

22. [22] K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, “FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media,” Big Data, vol. 8, no. 3, pp. 171–188, 2020, doi: 10.1089/big.2020.0062. [Google Scholar] [Crossref]

23. [23] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “Fake news detection on social media: A data mining perspective,” SIGKDD Explor. Newsl., vol. 19, no. 1, pp. 22–36, 2017, doi: 10.1145/3137597.3137600. [Google Scholar] [Crossref]

24. [24] 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: 10.1145/3395046. [Google Scholar] [Crossref]

25. [25] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, “Detection and resolution of rumours in social media: A survey,” ACM Comput. Surv., vol. 51, no. 2, pp. 1–36, 2018, doi: 10.1145/3161603. [Google Scholar] [Crossref]

26. [26] H. Rashkin, E. Choi, J. Y. Jang, S. Volkova, and Y. Choi, “Truth of varying shades: Analyzing language in fake news and political fact-checking,” in Proc. EMNLP, 2017, pp. 2931–2937, doi: 10.18653/v1/D17-1317. [Google Scholar] [Crossref]

27. [27] 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. Workshop Comput. Approaches to Deception Detection, 2016, pp. 7–17, doi: 10.18653/v1/W16-0802. [Google Scholar] [Crossref]

28. [28] F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein, “Fake news detection on social media using geometric deep learning,” arXiv:1902.06673, 2019. [Online]. Available: https://arxiv.org/abs/1902.06673 [Google Scholar] [Crossref]

29. [29] X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Inf. Process. Manage., vol. 57, no. 2, 102025, 2020, doi: 10.1016/j.ipm.2019.03.004. [Google Scholar] [Crossref]

30. [30] C. Bisaillon, “Fake and real news dataset,” Kaggle, 2020. [Online]. Available: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset [Google Scholar] [Crossref]

31. [31] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006, doi: 10.1016/j.patrec.2005.10.010. [Google Scholar] [Crossref]

32. [32] T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Comput., vol. 10, no. 7, pp. 1895–1923, 1998, doi: 10.1162/089976698300017197. [Google Scholar] [Crossref]

33. [33] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you? Explaining the predictions of any classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144, doi: 10.1145/2939672.2939778. [Google Scholar] [Crossref]

34. [34] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NeurIPS, 2017, pp. 4765–4774. [Online]. Available: https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions [Google Scholar] [Crossref]

35. [35] J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., vol. 13, pp. 281–305, 2012. [Online]. Available: https://jmlr.org/papers/v13/bergstra12a.html [Google Scholar] [Crossref]

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