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Accelerating dynamics of collective attention

Author

Listed:
  • Philipp Lorenz-Spreen

    (Technische Universität Berlin
    Center for Adaptive Rationality, Max Planck Institute for Human Development)

  • Bjarke Mørch Mønsted

    (Technical University of Denmark)

  • Philipp Hövel

    (Technische Universität Berlin
    University College Cork)

  • Sune Lehmann

    (Technical University of Denmark
    University of Copenhagen)

Abstract

With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals of collective attention.

Suggested Citation

  • Philipp Lorenz-Spreen & Bjarke Mørch Mønsted & Philipp Hövel & Sune Lehmann, 2019. "Accelerating dynamics of collective attention," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09311-w
    DOI: 10.1038/s41467-019-09311-w
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    Cited by:

    1. Matilde Giaccherini & Joanna Kopinska & Gabriele Rovigatti, 2022. "Vax Populi: The Social Costs of Online Vaccine Skepticism," CESifo Working Paper Series 10184, CESifo.
    2. David J. Grüning, 2022. "Synthesis of human and artificial intelligence: Review of “How to stay smart in a smart world: Why human intelligence still beats algorithms” by Gerd Gigerenzer," Futures & Foresight Science, John Wiley & Sons, vol. 4(3-4), September.
    3. Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Huanhuan Zheng & Tat-Seng Chua, 2024. "Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes," Papers 2410.17266, arXiv.org.
    4. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    5. Antonio, Fernando J. & Itami, Andreia S. & Dalmedico, Jônatas F. & Mendes, Renio S., 2022. "On the dynamics of reporting data: A case study of UFO sightings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    6. Minou Goetze & Christina Herdt & Ricarda Conrad & Stephan Stricker, 2023. "Preferences and Attitudes towards Debt Collection: A Cross-Generational Investigation," Papers 2303.05380, arXiv.org.
    7. Pardo Pintos, Alejandro & Shalom, Diego E. & Tagliazucchi, Enzo & Mindlin, Gabriel & Trevisan, Marcos, 2023. "A model of phase-coupled delay equations for the dynamics of word usage," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    8. Philipp Lorenz-Spreen & Stephan Lewandowsky & Cass R. Sunstein & Ralph Hertwig, 2020. "How behavioural sciences can promote truth, autonomy and democratic discourse online," Nature Human Behaviour, Nature, vol. 4(11), pages 1102-1109, November.

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