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Stiffness Analysis to Predict the Spread Out of Fake Information

Author

Listed:
  • Raffaele D’Ambrosio

    (Department of Information Engineering and Computer Science and Mathematics, University of L’Aquila, 67100 L’Aquila, Italy)

  • Giuseppe Giordano

    (Department of Mathematics, University of Salerno, 84084 Fisciano, Italy)

  • Serena Mottola

    (Department of Economic and Legal Studies, University “Parthenope” of Naples, 80133 Naples, Italy)

  • Beatrice Paternoster

    (Department of Mathematics, University of Salerno, 84084 Fisciano, Italy)

Abstract

This work highlights how the stiffness index, which is often used as a measure of stiffness for differential problems, can be employed to model the spread of fake news. In particular, we show that the higher the stiffness index is, the more rapid the transit of fake news in a given population. The illustration of our idea is presented through the stiffness analysis of the classical SIR model, commonly used to model the spread of epidemics in a given population. Numerical experiments, performed on real data, support the effectiveness of the approach.

Suggested Citation

  • Raffaele D’Ambrosio & Giuseppe Giordano & Serena Mottola & Beatrice Paternoster, 2021. "Stiffness Analysis to Predict the Spread Out of Fake Information," Future Internet, MDPI, vol. 13(9), pages 1-10, August.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:222-:d:624183
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    References listed on IDEAS

    as
    1. Quintino Francesco Lotito & Davide Zanella & Paolo Casari, 2021. "Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks," Future Internet, MDPI, vol. 13(3), pages 1-20, March.
    2. Fan, Dongmei & Jiang, Guo-Ping & Song, Yu-Rong & Li, Yin-Wei, 2020. "Novel fake news spreading model with similarity on PSO-based networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Dorje C. Brody & David M. Meier, 2018. "Mathematical models for fake news," Papers 1809.00964, arXiv.org, revised Nov 2021.
    4. Taichi Murayama & Shoko Wakamiya & Eiji Aramaki & Ryota Kobayashi, 2021. "Modeling the spread of fake news on Twitter," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    5. Nida Aslam & Irfan Ullah Khan & Farah Salem Alotaibi & Lama Abdulaziz Aldaej & Asma Khaled Aldubaikil & M. Irfan Uddin, 2021. "Fake Detect: A Deep Learning Ensemble Model for Fake News Detection," Complexity, Hindawi, vol. 2021, pages 1-8, April.
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