The growth of citations to published content typically follows an S-shaped curve. We look back at the fairly homogeneous citation-growth patterns — and at the few exceptions to them — for the content that we published in 2017.
Papers rack up citations — first slowly, and then rapidly. Eventually, the growth slows to a trickle. This general trend of how citations to a piece of work accrue can be described by a sigmoid (or logistic) function — that is, an S-shaped curve. In fact, S curves can reasonably describe many natural phenomena, as well as human-driven activities, involving slow–fast–slow patterns (for example, tumour growth, the kinetics of many reactions, a project’s progress, the adoption of technology, and the spread of information1).
Of course, the rates at which individual articles gather citations over time — and thus the actual shape and height of the corresponding S-shaped curve — can differ significantly. Papers with a larger natural audience — because they are particularly interdisciplinary or because they belong to large or ‘hot’ fields of research — will accrue citations at faster rates than articles with narrower implications or whose results are mostly relevant to a niche community.
Yet, how heterogeneous are the actual shapes of the citation curves for papers from different disciplines and fields of research? And how big is the fraction of papers with citation records that do not follow the typical sigmoid pattern? According to the brief history of the papers that Nature Biomedical Engineering published in its launch year, the answers are unexciting. The growth of citations to articles published in 2017 shows expected variabilities in citation growth (Fig. 1, left; the inset makes the beginning of the expected S-shaped curves visually apparent), and the patterns in annualized growth in citations are, perhaps unexpectedly, rather homogeneous (Fig. 1, right) — for about 80% of them (63 out of 79 articles), large annualized growth rates in the second year after publication (with respect to the first year) are followed by smaller growth rates in the third and fourth years.
Looking at the few atypical patterns may be more exciting. Which papers accrued citations faster later than expected (the so-called ‘sleeping beauties’;2 purple lines in Fig. 1), which (‘shooting star’) papers maintained their initial citation growth for longer (orange lines), and which (‘late bloomer’) articles (blue lines) had an unexpected bounce back from a steeper decline in citation growth rates? We did not find any clear patterns: the 16 articles with atypical citation trends (coloured lines) span disciplines, disease types and research emphases (strictly preclinical, translational, methodological, applicational and performance-based), are of varied content types (original research, commentary and scholarly reviews), and range widely in publication times (from December 2016 to December 2017), article views (from a few thousand to hundreds of thousands) and Altmetric scores (from 16 to over 1,000). Causes for the anomalous patterns may actually be mundane: probably due to chance; also, citations beget more citations; and, for articles that accumulate few citations, citation growth rates can be more prone to fluctuations, owing to variabilities arising from, for instance, publication timings and any errors and delays in the counting of citations.
Also, the five years of accumulated citations are ‘early days’. The trends in the data in Fig. 1 suggest that double-digit annualized citation growth rates may persist for many more tens of months. In fact, four years after publication, citations to the articles published in 2017 grew by 51% on median (interquartile range, 39–60%). It may thus take a while for these papers to reach the top of their S-shaped curve of citations.
What can we gather from this analysis? The sigmoidal spread of information can be explained by a model that accounts for fast information flow through established ‘long channels’ of users of the information and for the slow spread from them to new users1. For scientific articles, the ‘established users’ would mostly correspond to the article’s natural readers. We hope that the data in Fig. 1 reflect that the broader audience of Nature Biomedical Engineering is helping to diffuse the published interdisciplinary findings farther and wider.
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van Raan, A. F. J. & Winnink, J. J. PLoS ONE 14, e0223373 (2019).
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Five years of S-shaped citation patterns. Nat Biomed Eng 6, 1–2 (2022). https://doi.org/10.1038/s41551-022-00844-y
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DOI: https://doi.org/10.1038/s41551-022-00844-y