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The effects of information overload on online conversation dynamics

Author

Listed:
  • Chathika Gunaratne

    (University of Central Florida)

  • Nisha Baral

    (University of Central Florida)

  • William Rand

    (North Carolina State University)

  • Ivan Garibay

    (University of Central Florida)

  • Chathura Jayalath

    (University of Central Florida)

  • Chathurani Senevirathna

    (University of Central Florida)

Abstract

The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find that conversation volume and participation are lowest under high information overload thresholds and mid-range rates of information loss. Calibrating the model to user responsiveness data on Twitter, we replicate and explain several observed phenomena: (1) Responsiveness is sensitive to information overload threshold at high rates of information loss; (2) Information overload threshold and rate of information loss are Pareto-optimal and users may experience overload at inflows exceeding 30 notifications per hour; (3) Local abundance of small cascades of modest global popularity and local scarcity of larger cascades of high global popularity explains why overloaded users receive, but do not respond to large, highly popular cascades; 4) Users typically work with 7 notifications per hour; 5) Over-exposure to information can suppress the likelihood of response by overloading users, contrary to analogies to biologically-inspired viral spread. Reconceptualizing information spread with the mechanisms of information overload creates a richer representation of online conversation dynamics, enabling a deeper understanding of how (dis)information is transmitted over social media.

Suggested Citation

  • Chathika Gunaratne & Nisha Baral & William Rand & Ivan Garibay & Chathura Jayalath & Chathurani Senevirathna, 2020. "The effects of information overload on online conversation dynamics," Computational and Mathematical Organization Theory, Springer, vol. 26(2), pages 255-276, June.
    • Handle: RePEc:spr:comaot:v:26:y:2020:i:2:d:10.1007_s10588-020-09314-9
    DOI: 10.1007/s10588-020-09314-9
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    References listed on IDEAS

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    1. William Rand & Jeffrey Herrmann & Brandon Schein & Neža Vodopivec, 2015. "An Agent-Based Model of Urgent Diffusion in Social Media," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-1.
    2. Russell W. Belk, 2013. "Extended Self in a Digital World," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(3), pages 477-500.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Peter Gordon Roetzel, 2019. "Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework developmen," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 479-522, December.
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    Cited by:

    1. Lux Miranda & Ozlem Ozmen Garibary, 2023. "Approaching (super)human intent recognition in stag hunt with the Naïve Utility Calculus generative model," Computational and Mathematical Organization Theory, Springer, vol. 29(3), pages 434-447, September.

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