International Journal of Modern Science and Technology


​​May-June 2021, Vol. 6, No. 5-6, pp. 89-94. 

​​Fake news detection using machine learning

S. P. Sivananthan, S. T. Saravanan*, M. Udhai Ram
Department of Computer Science and Engineering, R. M. K. College of Engineering and Technology, Chennai, India.

​​*Corresponding author’s


The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain must explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

Keywords: Stochastic gradient descent; Term frequency-inverse document frequency; Linear support vector machine; Fake News.


  1. Douglas A. News consumption and the new electronic media. The International Journal of Press/Politics. 2006;11:29-52.
  2. Wong J. Almost all the traffic to fake news sites is from facebook. New data show, 2016.
  3. Lazer DMJ, Baum MA, Benkler Y, et al., The science of fake news. Science 2018;3591094–1096.
  4. Garc´ıa SA, Garc´ıa GG, Prieto MS, Guerrero AJM, Jime´nez CR. The impact of term fake news on the scientific community scientific performance and mapping in web of science. Social Sciences 2020;9:73.
  5. Holan AD. Lie of the Year: Fake News, Politifact, Washington, DC, USA, 2016.
  6. Kogan S, Moskowitz TJ, Niessner M. Fake News: Evidence from Financial Markets, 2019. abstract=3237763.
  7. Robb A. Anatomy of a fake news scandal,” Rolling Stone. 2017;1301:28–33.
  8. Soll J. The long and brutal history of fake news. Politico Magazine 2016;18.
  9. Hua J, Shaw R. Corona virus (covid-19) “infodemic” and emerging issues through a data lens: the case of China. International Journal of Environmental Research and Public Health 2020;17:2309.
  10. Conroy NK, Rubin VL, Chen Y. Automatic deception detection: methods for finding fake news. Proceedings of the Association for Information Science and Technology 2015;52:1–4.
  11. Asr FT, Taboada M. Misinfotext: a collection of news articles, with false and true labels, 2019.
  12. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media. ACM SIGKDD Explorations Newsletter 2017;19:22–36.
  13. Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science 2018;359:1146–1151.
  14. Allcott H, Gentzkow M. Social media and fake news inthe 2016 election. Journal of Economic Perspectives 2017;31:211–236.
  15. Rubin VL, Conroy N, Chen Y, Cornwell S. Fake news or truth? using satirical cues to detect potentially misleading news,” in Proceedings of the Second Workshop on Computa- tional Approaches to Deception Detection, San Diego,CA, USA, 2016, pp. 7–17.
  16. Jwa H, Oh D, Park K, Kang JM, Lim H. exBAKE: automatic fake news detection model based on bidirectional encoder representations from transformers (bert). Applied Sciences 2019;9:4062.
  17. Ahmed H, Traore I, Saad S. Detection of online fake news using n-gram analysis and machine learning techniques. Proceedings of the International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, pp. 127–138, Springer, Vancouver, Canada, 2017.
  18. Wang WY. Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection, Association for Computational Linguistics, Stroudsburg, PA, USA, 2017.
  19. Sriram A, Sudhakar TD. Technology revolution in the inspection of power transmission lines - A literature review. 7th International Conference on Electrical Energy Systems (ICEES), 2021, pp. 256-262.               doi: 10.1109/ICEES51510.2021.9383707.
  20. Anbalagan S, Sudhakar TD. Protection of Power Transmission Lines Using Intelligent Hot Spot Detection. Fifth International Conference on Electrical Energy Systems (ICEES), 2019, pp. 1-6.                                      doi: 10.1109/ICEES.2019.8719290
  21. Riedel B, Augenstein I, Spithourakis GP, Riedel S. A simple but tough-to-beat baseline for the fake news challenge stance detection task. 2017. 03264.

ISSN 2456-0235