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Using NER + ML to Automatically Detect Fake News

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

Abstract

In the overload information era, we need to be conscious of the dissemination of incoherent and misleading content both in the traditional and social media. It is a problem that has worsened recently and called the attention of some governments worldwide. The so-called Fake News has got notoriety due to the popularization and rapid consumption of online news. The democratization of the internet access carried out an increase in independent production and consumption of a variety of unverified information contents, which are also spread around on a large scale. Because the production capacity is much higher than that of the fact-checking agencies, it becomes necessary the support of systems for automatic detection of this type of content. Therefore, in this article, we propose a linguistic-structure analysis approach with named-entity recognition to identify fake news. By applying our approach, we can identify linguistic-structures that must unveil an article produced and verified by professional news agencies from that false information and sensationalist. In this regard, we present a linguistic analysis system with 90% on average accuracy of identification surpassing the state-of-the-art of this type of content in the literature datasets.

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Notes

  1. 1.

    https://www.camara.leg.br/propostas-legislativas/2256735.

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Acknowledgments

The authors acknowledge the Research Support Foundation of Espírito Santo (FAPES, process 80136451) for the research support grant.

Author information

Authors and Affiliations

  1. Postgraduate Program in Informatics – Federal University of Espírito Santo (UFES), Vitória, Brazil

    Marcos A. Spalenza & Elias de Oliveira

  2. NCE, Tércio Pacitti Institute – Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

    Priscila M. V. Lima

  3. PESC/COPPE – Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

    Leopoldo Lusquino-Filho, Priscila M. V. Lima & Felipe M. G. França

Authors
  1. Marcos A. Spalenza
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  2. Elias de Oliveira
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  3. Leopoldo Lusquino-Filho
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  4. Priscila M. V. Lima
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  5. Felipe M. G. França
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Spalenza, M.A., de Oliveira, E., Lusquino-Filho, L., Lima, P.M.V., França, F.M.G. (2021). Using NER + ML to Automatically Detect Fake News. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_109

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