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Fake News Detection Using Time Series and User Features Classification

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Applications of Evolutionary Computation (EvoApplications 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

Abstract

In a scenario where more and more individuals use online social network platforms as an instrument to propagate news without any control, it is necessary to design and implement new methods and techniques that guarantee the veracity of the disseminated news. In this paper, we propose a method to classify true and false news, commonly known as fake news, which exploits time series-based features extracted from the evolution of news, and features from the users involved in the news spreading. Applying our methodology over a real Twitter dataset of precategorized true and false news, we have obtained an accuracy of 84.61% in 10-fold cross-validation, and proved experimentally that all the selected features are relevant for this classification task.

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Notes

  1. 1.

    The names of fields has been extracted from the Twitter developers documentation.

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Acknowledgements

This work has been supported by several research grants: Spanish Ministry of Science and Education under TIN2014-56494-C4-4-P grant (DeepBio), European Union, under ISFP-POLICE ACTION: 823701-ISFP-2017-AG-RAD grant (YoungRes), and Comunidad Autónoma de Madrid under P2018/TCS-4566 grant (CYNAMON).

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Authors and Affiliations

  1. Dip. di Ingegneria Elettrica, Elettronica e Informatica (DIEEI), Università degli Studi di Catania, Catania, Italy

    Marialaura Previti & Michele Malgeri

  2. Universidad Autónoma de Madrid, Madrid, Spain

    Victor Rodriguez-Fernandez

  3. Departamento de Sistemas Informaticos, Technical University of Madrid, Madrid, Spain

    David Camacho

  4. Dip. di Matematica e Informatica (DMI), Università degli Studi di Catania, Catania, Italy

    Vincenza Carchiolo

Authors
  1. Marialaura Previti
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  2. Victor Rodriguez-Fernandez
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  3. David Camacho
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  4. Vincenza Carchiolo
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  5. Michele Malgeri
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Corresponding author

Correspondence to Victor Rodriguez-Fernandez .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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Previti, M., Rodriguez-Fernandez, V., Camacho, D., Carchiolo, V., Malgeri, M. (2020). Fake News Detection Using Time Series and User Features Classification. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_22

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43721-3

  • Online ISBN: 978-3-030-43722-0

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