IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v26y2020i2d10.1007_s10588-020-09314-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-020-09314-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-020-09314-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Barsha Saha & Miguel Martínez-García & Sharad Nath Bhattacharya & Rohit Joshi, 2022. "Overcoming Choice Inertia through Social Interaction—An Agent-Based Study of Mobile Subscription Decision," Games, MDPI, vol. 13(3), pages 1-16, June.
    2. William Rand & Christian Stummer, 2021. "Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms," Annals of Operations Research, Springer, vol. 305(1), pages 425-447, October.
    3. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    4. Shari, Babajide Epe & Dioha, Michael O. & Abraham-Dukuma, Magnus C. & Sobanke, Victor O. & Emodi, Nnaemeka V., 2022. "Clean cooking energy transition in Nigeria: Policy implications for Developing countries," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 319-343.
    5. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    6. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    7. Simon P. Anderson & André de Palma, 2012. "Competition for attention in the Information (overload) Age," RAND Journal of Economics, RAND Corporation, vol. 43(1), pages 1-25, March.
    8. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    9. Klingler, Anna-Lena & Luthander, Rasmus, 2018. "Market diffusion of residential PV and battery systems driven by self-consumption: A comparison of Sweden and Germany," Working Papers "Sustainability and Innovation" S18/2018, Fraunhofer Institute for Systems and Innovation Research (ISI).
    10. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    11. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    12. White, Reilly & Marinakis, Yorgos & Islam, Nazrul & Walsh, Steven, 2020. "Is Bitcoin a currency, a technology-based product, or something else?," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    13. Shigeno, Hidenori & Matsuzaki, Taisuke & Ueki, Yasushi & Tsuji, Masatsugu, 2023. "The Effect of the Covid-19 Pandemic on the Innovation Process of Small and Medium-sized Regional Firms," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 278018, International Telecommunications Society (ITS).
    14. Sohn, So Young & Lim, Michael, 2008. "The effect of forecasting and information sharing in SCM for multi-generation products," European Journal of Operational Research, Elsevier, vol. 186(1), pages 276-287, April.
    15. Day Yang Liu & Wen Chun Tsai & Pei Leen Liu & Chung Yi Fang, 2021. "Determinants of sales revenue in innovation diffusion effects of Taiwan sports lottery during the FIFA World Cup 2018," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 43-58, June.
    16. Yoshida, Masayuki & James, Jeffrey D. & Cronin, J. Joseph, 2013. "Sport event innovativeness: Conceptualization, measurement, and its impact on consumer behavior," Sport Management Review, Elsevier, vol. 16(1), pages 68-84.
    17. Marie-Estelle Binet & Lionel Richefort, 2011. "Diffusion of irrigation technologies: the role of mimicking behaviour and public incentives," Applied Economics Letters, Taylor & Francis Journals, vol. 18(1), pages 43-48.
    18. Lutz, Christoph & Newlands, Gemma, 2018. "Consumer segmentation within the sharing economy: The case of Airbnb," Journal of Business Research, Elsevier, vol. 88(C), pages 187-196.
    19. R. Bentley & Michael O’Brien & Paul Ormerod, 2011. "Quality versus mere popularity: a conceptual map for understanding human behavior," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 10(2), pages 181-191, December.
    20. Jingjing Zhao & Yongli Li & Yunlong Ding & Chao Liu, 2019. "The value of leading customers in a crowdfunding-based marketing pattern," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-18, April.

    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:spr:comaot:v:26:y:2020:i:2:d:10.1007_s10588-020-09314-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.