IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i9p222-d624183.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/9/222/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/13/9/222/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jamalzadeh, Saeed & Mettenbrink, Lily & Barker, Kash & González, Andrés D. & Radhakrishnan, Sridhar & Johansson, Jonas & Bessarabova, Elena, 2024. "Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Lv, Xijian & Fan, Dongmei & Yang, Junxian & Li, Qiang & Zhou, Li, 2024. "Delay differential equation modeling of social contagion with higher-order interactions," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    3. Noha Alnazzawi & Najlaa Alsaedi & Fahad Alharbi & Najla Alaswad, 2022. "Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus," Data, MDPI, vol. 7(4), pages 1-13, April.
    4. Paul Meddeb & Stefan Ruseti & Mihai Dascalu & Simina-Maria Terian & Sebastien Travadel, 2022. "Counteracting French Fake News on Climate Change Using Language Models," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    5. J. Franceschi & L. Pareschi & M. Zanella, 2022. "From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications," Partial Differential Equations and Applications, Springer, vol. 3(6), pages 1-26, December.
    6. Ciprian-Octavian Truică & Elena-Simona Apostol, 2023. "It’s All in the Embedding! Fake News Detection Using Document Embeddings," Mathematics, MDPI, vol. 11(3), pages 1-29, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:222-:d:624183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.