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Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model

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  • De Martino, Giuseppe
  • Spina, Serena

Abstract

We construct a news spreading model with a time dependent contact rate which generalizes the classical Susceptible–Infected model of epidemiology. In particular, we are interested on the time-dynamics of the sharing and diffusion process of news on the Internet. We focus on the counting process describing the number of connections to a given website, characterizing the cumulative density function of its inter-arrival times. Moreover, starting from the general form of the finite dimensional distribution of the process, we determine a formula for the time-variable rate of the connections and establish its relationship with the probability density function of the interarrival times. We finally show the effectiveness of our theoretical framework analyzing a real-world dataset, the Memetracker dataset, whose parameters characterizing the diffusion process are determined.

Suggested Citation

  • De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    • Handle: RePEc:eee:phsmap:v:438:y:2015:i:c:p:634-644
    DOI: 10.1016/j.physa.2015.07.022
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    References listed on IDEAS

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    Cited by:

    1. Antonio Di Crescenzo & Paola Paraggio & Serena Spina, 2023. "Stochastic Growth Models for the Spreading of Fake News," Mathematics, MDPI, vol. 11(16), pages 1-23, August.
    2. Maria Gamboa & Maria Jesus Lopez-Herrero, 2018. "On the Number of Periodic Inspections During Outbreaks of Discrete-Time Stochastic SIS Epidemic Models," Mathematics, MDPI, vol. 6(8), pages 1-13, July.
    3. Giorno, Virginia & Spina, Serena, 2016. "Rumor spreading models with random denials," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 569-576.

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