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On the statistical properties of viral misinformation in online social media

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  • Bessi, Alessandro

Abstract

The massive diffusion of online social media allows for the rapid and uncontrolled spreading of conspiracy theories, hoaxes, unsubstantiated claims, and false news. Such an impressive amount of misinformation can influence policy preferences and encourage behaviors strongly divergent from recommended practices. In this paper, we study the statistical properties of viral misinformation in online social media. By means of methods belonging to Extreme Value Theory, we show that the number of extremely viral posts over time follows a homogeneous Poisson process, and that the interarrival times between such posts are independent and identically distributed, following an exponential distribution. Moreover, we characterize the uncertainty around the rate parameter of the Poisson process through Bayesian methods. Finally, we are able to derive the predictive posterior probability distribution of the number of posts exceeding a certain threshold of shares over a finite interval of time.

Suggested Citation

  • Bessi, Alessandro, 2017. "On the statistical properties of viral misinformation in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 459-470.
    • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:459-470
    DOI: 10.1016/j.physa.2016.11.012
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    References listed on IDEAS

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    1. Cirillo, Pasquale, 2013. "Are your data really Pareto distributed?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5947-5962.
    2. Alessandro Bessi & Mauro Coletto & George Alexandru Davidescu & Antonio Scala & Guido Caldarelli & Walter Quattrociocchi, 2015. "Science vs Conspiracy: Collective Narratives in the Age of Misinformation," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    3. Giovanni Luca Ciampaglia & Prashant Shiralkar & Luis M Rocha & Johan Bollen & Filippo Menczer & Alessandro Flammini, 2015. "Computational Fact Checking from Knowledge Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-13, June.
    4. Javarone, Marco Alberto, 2014. "Social influences in opinion dynamics: The role of conformity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 19-30.
    5. Fabiana Zollo & Petra Kralj Novak & Michela Del Vicario & Alessandro Bessi & Igor Mozetič & Antonio Scala & Guido Caldarelli & Walter Quattrociocchi, 2015. "Emotional Dynamics in the Age of Misinformation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-22, September.
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    Cited by:

    1. Tiantian Zhang & Zhiyong Zhang & Kejing Zhao & Brij B. Gupta & Varsha Arya, 2023. "A Lightweight Cross-Domain Authentication Protocol for Trusted Access to Industrial Internet," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-25, January.
    2. Talwar, Shalini & Dhir, Amandeep & Kaur, Puneet & Zafar, Nida & Alrasheedy, Melfi, 2019. "Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 72-82.
    3. Ozbay, Feyza Altunbey & Alatas, Bilal, 2020. "Fake news detection within online social media using supervised artificial intelligence algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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