Modelling of the Fake Posting Recognition in On-Line Media Using Machine Learning

  • Kristína MachováEmail author
  • Marián Mach
  • Gabriela Demková
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12011)


Discuss content in the online web space has a significant impact on social life in recent years, especially in the political world. The impact of social networks has its advantages and disadvantages. An important disadvantage is a rising of the antisocial content in online communities. The antisocial content represents a serious and actual problem that is reinforced by a simplifying the process of creating and disseminating of antisocial posts. A typical example is a spreading of fake reviews. Detection of fake reviews is becoming one of the most important areas of research in last years. It is easier to track the impact of fake reviews than to detect them. The aim of this paper is to create suitable models for the fake reviews recognition using machine learning algorithms particularly decision tree, random forests, support vector machine and naïve Bayes classifier. Using a confusion matrix, several indicators of binary classification efficiency were quantified in the process of these models testing.


Social media mining Model for fake reviews identification Machine learning methods Antisocial posting 



The work presented in this paper was supported by the Slovak Research and Development Agency under the contract APVV-017-0267 and APVV-16-0213.


  1. 1.
    Vítek, F.: Fake news – where did it begin and where do we go?, May 2019.
  2. 2.
    Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. Newsletter 19(1), 22–36 (2017)Google Scholar
  3. 3.
    Dematis, I., Karapistoli, E., Vakali, A.: Fake review detection via exploitation of spam indicators and reviewer behavior characteristics. In: Tjoa, A.M., Bellatreche, L., Biffl, S., van Leeuwen, J., Wiedermann, J. (eds.) SOFSEM 2018. LNCS, vol. 10706, pp. 581–595. Springer, Cham (2018). Scholar
  4. 4.
    Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using N-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127–138. Springer, Cham (2017). Scholar
  5. 5.
    Chowdhary, N.S., Pandit, A.A.: Fake review detection using classification. Int. J. Comput. Appl. 180(50), 16–21 (2018)Google Scholar
  6. 6.
    Cardoso, E.F., Silva, R.M., Almeida, T.A.: Towards automatic filtering of fake reviews. Neurocomputing 309, 1–41 (2018)CrossRefGoogle Scholar
  7. 7.
    Russell, S.J., Norvig, P.: Artificial Intelligence. A Modern Approach, 3rd edn, pp. 1–932. Prentice Hall, Pearson Education, Upper Saddle River (2010). ISBN-13: 978-0-13-604259-4zbMATHGoogle Scholar
  8. 8.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn, pp. 1–703. Morgan Kaufmann, Elsevier, Burlington (2012)CrossRefGoogle Scholar
  9. 9.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, pp. 79–86 (2002)Google Scholar
  10. 10.
    Kingsford, C., Salzberg, S.L.: What are decision trees? Nat. Biotechnol. 26(1), 1011–1013 (2008)CrossRefGoogle Scholar
  11. 11.
    Orphanos, G., Kalles, D., Papagelis, T., Christodoulakis, D.: Decision trees and NLP: a case study in POS tagging. Academia, 1–7 (1999)Google Scholar
  12. 12.
    Magerman, D.M.: Statistical decision-tree models for parsing. In: Proceeding ACL 1995 Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, pp. 276–283 (1995)Google Scholar
  13. 13.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  14. 14.
    James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: With Applications in R. STS, vol. 103, pp. 1–426. Springer, New York (2013). Scholar
  15. 15.
    Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support vector clustering. J. Mach. Learn. Res. 2(2), 125–137 (2001)zbMATHGoogle Scholar
  16. 16.
    Paralič, J., et al.: Mining Knowledge from Texts. Equilibria, Košice (2010)Google Scholar
  17. 17.
    Mikula, M., Machová, K.: Combined approach for sentiment analysis in Slovak using a dictionary annotated by particle swarm optimization. Acta Electrotechnica et Informatica 18(2), 27–34 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kristína Machová
    • 1
    Email author
  • Marián Mach
    • 1
  • Gabriela Demková
    • 1
  1. 1.Department of Cybernetics and Artificial IntelligenceTechnical UniversityKošiceSlovakia

Personalised recommendations