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The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics

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  • Koon-Kiu Yan
  • Mark Gerstein

Abstract

The presence of web-based communities is a distinctive signature of Web 2.0. The web-based feature means that information propagation within each community is highly facilitated, promoting complex collective dynamics in view of information exchange. In this work, we focus on a community of scientists and study, in particular, how the awareness of a scientific paper is spread. Our work is based on the web usage statistics obtained from the PLoS Article Level Metrics dataset compiled by PLoS. The cumulative number of HTML views was found to follow a long tail distribution which is reasonably well-fitted by a lognormal one. We modeled the diffusion of information by a random multiplicative process, and thus extracted the rates of information spread at different stages after the publication of a paper. We found that the spread of information displays two distinct decay regimes: a rapid downfall in the first month after publication, and a gradual power law decay afterwards. We identified these two regimes with two distinct driving processes: a short-term behavior driven by the fame of a paper, and a long-term behavior consistent with citation statistics. The patterns of information spread were found to be remarkably similar in data from different journals, but there are intrinsic differences for different types of web usage (HTML views and PDF downloads versus XML). These similarities and differences shed light on the theoretical understanding of different complex systems, as well as a better design of the corresponding web applications that is of high potential marketing impact.

Suggested Citation

  • Koon-Kiu Yan & Mark Gerstein, 2011. "The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-7, May.
    • Handle: RePEc:plo:pone00:0019917
    DOI: 10.1371/journal.pone.0019917
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    References listed on IDEAS

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    Cited by:

    1. Xiaoxi Ling & Yu Liu & Zhen Huang & Parantu K. Shah & Cheng Li, 2016. "A graphical article-level metric for intuitive comparison of large-scale literatures," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 41-50, January.
    2. Lutz Bornmann, 2015. "Alternative metrics in scientometrics: a meta-analysis of research into three altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1123-1144, June.
    3. Liwen Vaughan & Juan Tang & Rongbin Yang, 2017. "Investigating disciplinary differences in the relationships between citations and downloads," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1533-1545, June.
    4. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.
    5. Mojisola Erdt & Aarthy Nagarajan & Sei-Ching Joanna Sin & Yin-Leng Theng, 2016. "Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1117-1166, November.
    6. Thomy Tonia & Herman Van Oyen & Anke Berger & Christian Schindler & Nino Künzli, 2016. "If I tweet will you cite? The effect of social media exposure of articles on downloads and citations," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 61(4), pages 513-520, May.

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