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The rise of hyperprolific authors in computer science: characterization and implications

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Abstract

In this article we study and characterize the phenomenon of the hyperprolific authors, who are the most productive researchers according to a given repository in a specific period of time. Particularly, we are interested in investigating and characterizing a subset of such hyperprolific authors who present a sudden growth in the number of published articles and coauthors, as well as concentrate their publications in a few specific journals, what can be seen as an anomalous behavior. Using data collected from the DBLP repository and covering the last 10 years, we propose a set of discriminative dimensions (features) aimed at characterizing the behavior of hyperprolific authors, ultimately helping to identify anomalous ones. Moreover, using a strategy based on ranking aggregation to identify the most prominent anomalous authors, we demonstrate that the best dimensions to characterize such anomalous behaviors may vary significantly among authors, but it is possible to identify a clear subset of them who present such behavior. Our results show that the top-ranked (most anomalous) authors manifest a distinct behavior from the middle-ranked ones. Indeed, each one of the five most anomalous authors published more than 48 journal articles in 2021 while collaborating with more than 1,000 coauthors in their careers. Specifically, one of such authors published more than 140 articles in just a single journal.

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Notes

  1. http://uis.unesco.org/apps/visualisations/research-and-development-spending, accessed on April 25, 2022.

  2. https://ncses.nsf.gov/pubs/nsb20206/publication-output-by-region-country-or-economy, accessed on April 25, 2022.

  3. http://dblb.org, accessed on May 14, 2022.

  4. https://scholar.google.com, accessed on May 14, 2022.

  5. https://arxiv.org, accessed on May 14, 2022.

  6. https://pubmed.ncbi.nlm.nih.gov, accessed on May 14, 2022.

  7. https://www.aminer.org, accessed on May 15, 2022.

  8. In this article we may use the words “researcher” and “author” to refer to a same person depending on its role when discussing a specific issue.

  9. We have computed those numbers based on the dataset we have created for our experiments. See Subsection 3.1.

  10. The Empirical Cumulative Distribution Function (ECDF) value for a given point p in the horizontal axis is the fraction of observations of the variable with values less than or equal to p.

  11. https://scholar.google.com/intl/en/scholar/citations.html, accessed on May 16, 2022.

  12. https://www.microsoft.com/en-us/research/project/microsoft-academic-graph, accessed on June 25, 2022.

  13. https://jcr.clarivate.com, accessed on June 19, 2022.

  14. https://incites.help.clarivate.com/Content/Indicators-Handbook/ih-journal-impact-factor.htm, accessed on June 25, 2022.

  15. A mega journal is a type of journal where publishers charge authors, rather than readers, for the article publication.

  16. https://predatory-publishing.com/how-many-predatory-journals-are-there, accessed on June 19, 2022.

  17. https://www.interacademies.org, accessed on May 9, 2022.

  18. Five temporal scenarios \(\times\) four temporal metrics \(\times\) two summarizations \(+\) the entropy \(+\) the publication intensity.

  19. This is the ECDF’s value obtained by subtracting 0.31 (or 31%) from one, which is equivalent to the portion of the reference set’s plot at the right side of the vertical dotted line.

  20. Remind that we use the term hyperproductive for the set of the top most productive researchers of the considered time period.

  21. For the sake of clarity, we limit the graph plot to the 10 top-ranked researchers only. The same reasoning applies to the analysis of the features discussed next.

  22. We consider ties as sharing the same rank.

  23. https://www.andrews.edu/\(\sim\) calkins/math/edrm611/edrm05.htm, accessed on January 1, 2022.

  24. All resources needed to reproduce our experiments, including our source code and dataset, are available at https://github.com/edreqm/raise-of-hiperprolific.

  25. We take the working days in 2021 as a reference and repeat the same number for all the 10 years. We take the 365 year’s days and exclude the weekends (104). To be conservative, we do not exclude the holidays.

  26. To compute the topics, we first concatenate all the publications’ titles to create a document for each author. Then, we eliminate all the adverbs, non-English, and stop words. Finally, we applied the CluWords algorithm to discover the 12 topics and the top 10 words describing them. We chose 12 as the number of topics to resemble the 12 Computer Science subfields defined in https://en.wikipedia.org/wiki/Outline_of_computer_science, accessed on December 11, 2022).

  27. https://pubmed.ncbi.nlm.nih.gov, accessed on June 27, 2022

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Acknowledgements

This work is partially supported by the authors individual research grants from CAPES, CNPq and FAPEMIG, and by the projects MASWeb and INCT-Cyber.

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  1. Wagner Meira Jr., Marcos André Gonçalves, and Alberto H. F. Laender have contributed equally to this work.

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  1. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil

    Edré Moreira, Wagner Meira Jr., Marcos André Gonçalves & Alberto H. F. Laender

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Moreira, E., Meira, W., Gonçalves, M.A. et al. The rise of hyperprolific authors in computer science: characterization and implications. Scientometrics 128, 2945–2974 (2023). https://doi.org/10.1007/s11192-023-04676-8

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