Machine Learning Methods for Fake News Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users. Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions, e.g., during the election. Therefore, the methods which can effectively detect fake news are the focus of intense research. This work focuses on fake news detection in articles published online and on the basis of extensive research we confirmed that chosen machine learning algorithms can distinguish them from reliable information.


Fake news Online disinformation Classification Classifier ensebles Random Subspace 



This work is funded under SocialTruth project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477.


  1. 1.
    Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. In: Proceedings of the 2012 IEEE Symposium on Security and Privacy, SP 2012, Washington, DC, USA, pp. 461–475. IEEE Computer Society (2012).
  2. 2.
    Atodiresei, C.S., Tănăselea, A., Iftene, A.: Identifying fake news and fake users on Twitter. Procedia Comput. Sci. 126, 451–461 (2018). Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018, Belgrade, SerbiaCrossRefGoogle Scholar
  3. 3.
    Bondielli, A., Marcelloni, F.: A survey on fake news and rumour detection techniques. Inf. Sci. 497, 38–55 (2019). Scholar
  4. 4.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 675–684. ACM, New York (2011).,
  5. 5.
    Chen, C., Wu, K., Venkatesh, S., Zhang, X.: Battling the internet water army: detection of hidden paid posters. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 116–120 (2011)Google Scholar
  6. 6.
    Choraś, M., Giełczyk, A., Demestichas, K., Puchalski, D., Kozik, R.: Pattern recognition solutions for fake news detection. In: Saeed, K., Homenda, W. (eds.) CISIM 2018. LNCS, vol. 11127, pp. 130–139. Springer, Cham (2018). Scholar
  7. 7.
    Choraś, M., Pawlicki, M., Kozik, R., Demestichas, K., Kosmides, P., Gupta, M.: Socialtruth project approach to online disinformation (fake news) detection and mitigation. In: Proceedings of the 14th International Conference on Availability, Reliability and Security, p. 68. ACM (2019)Google Scholar
  8. 8.
    Conroy, N., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assoc. Inf. Sci. Technol. 52, 1–4 (2015). Scholar
  9. 9.
    Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016). Scholar
  10. 10.
    Giełczyk, A., Wawrzyniak, R., Choraś, M.: Evaluation of the existing tools for fake news detection. In: Saeed, K., Chaki, R., Janev, V. (eds.) CISIM 2019. LNCS, vol. 11703, pp. 144–151. Springer, Cham (2019). Scholar
  11. 11.
    Gravanis, G., Vakali, A., Diamantaras, K., Karadais, P.: Behind the cues: a benchmarking study for fake news detection. Expert Syst. Appl. 128, 201–213 (2019). Scholar
  12. 12.
    Horne, B.D., Adali, S.: This just. In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News. CoRR abs/1703.09398 (2017).
  13. 13.
    Jin, Z., Cao, J., Zhang, Y., Zhou, J., Tian, Q.: Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimedia 19(3), 598–608 (2017). Scholar
  14. 14.
    Ksieniewicz, P.: Combining Random Subspace approach with smote oversampling for imbalanced data classification. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS, pp. 660–673. Springer, Cham (2019). Scholar
  15. 15.
    Ksieniewicz, P., Woźniak, M.: Dealing with the task of imbalanced, multidimensional data classification using ensembles of exposers. In: First International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp. 164–175 (2017)Google Scholar
  16. 16.
    Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., Liu, Y.: Combating fake news: a survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol. 10(3), 21:1–21:42 (2019). Scholar
  17. 17.
    Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. SIGKDD Explor. Newsl. 19(1), 22–36 (2017). Scholar
  18. 18.
    Zhang, X., Ghorbani, A.A.: An overview of online fake news: characterization, detection, and discussion. Inf. Process. Manag. (2019).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Systems and Computer Networks, Faculty of ElectronicsWrocław University of Science and TechnologyWrocławPoland
  2. 2.Department of Teleinformatics SystemsUTP University of Science and TechnologyBydgoszczPoland

Personalised recommendations