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Supervised Classification Methods for Fake News Identification

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12416))

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Abstract

Along with the rapid increase in the popularity of online media, the proliferation of fake news and its propagation is also rising. Fake news can propagate with an uncontrollable speed without verification and can cause severe damages. Various machine learning and deep learning approaches have been attempted to classify the real and the false news. In this research, the author group presents a comprehensive performance evaluation of eleven supervised algorithms on three datasets for fake news classification.

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Notes

  1. 1.

    https://www.kaggle.com/mrisdal/fake-news.

References

  1. Ahmed, H., Traore, I., Saad, S.: Detecting opinion spams and fake news using text classification. Secur. Priv. 1(1), e9 (2018)

    Article  Google Scholar 

  2. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–36 (2017)

    Article  Google Scholar 

  3. Dadgar, S.M.H., Araghi, M.S., Farahani, M.M.: A novel text mining approach based on TF-IDF and support vector machine for news classification. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 112–116. IEEE (2016)

    Google Scholar 

  4. Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014)

  5. Granik, M., Mesyura, V.: Fake news detection using naive bayes classifier. In: 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), pp. 900–903. IEEE (2017)

    Google Scholar 

  6. Long, Y., Lu, Q., Xiang, R., Li, M., Huang, C.R.: Fake news detection through multi-perspective speaker profiles. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 252–256 (2017)

    Google Scholar 

  7. Ramos, J., et al.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, Piscataway, NJ, vol. 242, pp. 133–142 (2003)

    Google Scholar 

  8. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)

    Article  Google Scholar 

  9. Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond Accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006). https://doi.org/10.1007/11941439_114

    Chapter  Google Scholar 

  10. Thanh, C., Zelinka, I.: A survey on artificial intelligence in malware as next-generation threats. MENDEL 25(2), 27–34 (2019). https://doi.org/10.13164/mendel.2019.2.027

    Article  Google Scholar 

  11. Truong, T.C., Diep, Q.B., Zelinka, I.: Artificial intelligence in the cyber domain: offense and defense. Symmetry 12(3), 410 (2020). https://doi.org/10.3390/sym12030410

    Article  Google Scholar 

  12. Truong, T.C., Zelinka, I., Plucar, J., Čandík, M., Šulc, V.: Artificial intelligence and cybersecurity: past, presence, and future. In: Dash, S.S., Lakshmi, C., Das, S., Panigrahi, B.K. (eds.) Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp. 351–363. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0199-9_30

    Chapter  Google Scholar 

  13. Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 977–984 (2006)

    Google Scholar 

  14. Wang, W.Y.: “Liar, Liar pants on fire”: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648 (2017)

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Acknowledgement

The following grants are acknowledged for the financial support provided for this research: Grant of SGS No. SP2020/78, VSB Technical University of Ostrava.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00, Ostrava-Poruba, Ostrava, Czech Republic

    Thanh Cong Truong, Quoc Bao Diep & Ivan Zelinka

  2. Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Roman Senkerik

Authors
  1. Thanh Cong Truong
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  2. Quoc Bao Diep
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  3. Ivan Zelinka
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  4. Roman Senkerik
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Corresponding author

Correspondence to Thanh Cong Truong .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Truong, T.C., Diep, Q.B., Zelinka, I., Senkerik, R. (2020). Supervised Classification Methods for Fake News Identification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_40

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  • DOI: https://doi.org/10.1007/978-3-030-61534-5_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61533-8

  • Online ISBN: 978-3-030-61534-5

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