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Modeling the infectiousness of Twitter hashtags

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  • Skaza, Jonathan
  • Blais, Brian

Abstract

This study applies dynamical and statistical modeling techniques to quantify the proliferation and popularity of trending hashtags on Twitter. Using time-series data reflecting actual tweets in New York City and San Francisco, we present estimates for the dynamics (i.e., rates of infection and recovery) of several hundred trending hashtags using an epidemic modeling framework coupled with Bayesian Markov Chain Monte Carlo (MCMC) methods. This methodological strategy is an extension of techniques traditionally used to model the spread of infectious disease. Using SIR-type models, we demonstrate that most hashtags are marginally infectious, while very few emerge as “trending”. In doing so we illustrate that hashtags can be grouped by infectiousness, possibly providing a method for quantifying the trendiness of a topic.

Suggested Citation

  • Skaza, Jonathan & Blais, Brian, 2017. "Modeling the infectiousness of Twitter hashtags," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 289-296.
    • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:289-296
    DOI: 10.1016/j.physa.2016.08.038
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    References listed on IDEAS

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    Cited by:

    1. Tingqiang Chen & Lei Wang & Jining Wang & Qi Yang, 2017. "A Network Diffusion Model of Food Safety Scare Behavior considering Information Transparency," Complexity, Hindawi, vol. 2017, pages 1-16, December.
    2. Qi Yang & Yuejuan Hou & Haoran Wei & Tingqiang Chen & Jining Wang, 2022. "Nonlinear Diffusion Evolution Model of Unethical Behavior among Green Food Enterprise," Sustainability, MDPI, vol. 14(23), pages 1-22, December.
    3. Wang, Lei & Li, Shouwei & Chen, Tingqiang, 2019. "Investor behavior, information disclosure strategy and counterparty credit risk contagion," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 37-49.

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