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Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions

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  • Thelwall, Mike

Abstract

Although statistical models fit many citation data sets reasonably well with the best fitting models being the hooked power law and discretised lognormal distribution, the fits are rarely close. One possible reason is that there might be more uncited articles than would be predicted by any model if some articles are inherently uncitable. Using data from 23 different Scopus categories, this article tests the assumption that removing a proportion of uncited articles from a citation dataset allows statistical distributions to have much closer fits. It also introduces two new models, zero inflated discretised lognormal distribution and the zero inflated hooked power law distribution and algorithms to fit them. In all 23 cases, the zero inflated version of the discretised lognormal distribution was an improvement on the standard version and in 16 out of 23 cases the zero inflated version of the hooked power law was an improvement on the standard version. Without zero inflation the discretised lognormal models fit the data better than the hooked power law distribution 6 out of 23 times and with it, the discretised lognormal models fit the data better than the hooked power law distribution 9 out of 23 times. Apparently uncitable articles seem to occur due to the presence of academic-related magazines in Scopus categories. In conclusion, future citation analysis and research indicators should take into account uncitable articles, and the best fitting distribution for sets of citation counts from a single subject and year is either the zero inflated discretised lognormal or zero inflated hooked power law.

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  • Thelwall, Mike, 2016. "Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions," Journal of Informetrics, Elsevier, vol. 10(2), pages 622-633.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:2:p:622-633
    DOI: 10.1016/j.joi.2016.04.014
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    Cited by:

    1. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
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    3. Katchanov, Yurij L. & Markova, Yulia V. & Shmatko, Natalia A., 2023. "Uncited papers in the structure of scientific communication," Journal of Informetrics, Elsevier, vol. 17(2).
    4. Mike Thelwall, 2019. "The influence of highly cited papers on field normalised indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 519-537, February.
    5. Mrowinski, Maciej J. & Gagolewski, Marek & Siudem, Grzegorz, 2022. "Accidentality in journal citation patterns," Journal of Informetrics, Elsevier, vol. 16(4).
    6. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
    7. Zewen Hu & Yishan Wu & Jianjun Sun, 2018. "A quantitative analysis of determinants of non-citation using a panel data model," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 843-861, August.
    8. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2017. "The scaling relationship between degree centrality of countries and their citation-based performance on Management Information Systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1285-1299, September.
    9. Mike Thelwall & Kayvan Kousha & Mahshid Abdoli, 2017. "Is medical research informing professional practice more highly cited? Evidence from AHFS DI Essentials in drugs.com," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 509-527, July.
    10. Banshal, Sumit Kumar & Gupta, Solanki & Lathabai, Hiran H & Singh, Vivek Kumar, 2022. "Power Laws in altmetrics: An empirical analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    11. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2018. "The power law relationship between citation impact and multi-authorship patterns in articles in Information Science & Library Science journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 919-932, March.
    12. Thelwall, Mike & Fairclough, Ruth, 2017. "The accuracy of confidence intervals for field normalised indicators," Journal of Informetrics, Elsevier, vol. 11(2), pages 530-540.
    13. Jeppe Nicolaisen & Tove Faber Frandsen, 2019. "Zero impact: a large-scale study of uncitedness," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1227-1254, May.
    14. Mike Thelwall, 2017. "Are Mendeley reader counts useful impact indicators in all fields?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1721-1731, December.
    15. Guillermo Armando Ronda-Pupo, 2017. "The citation-based impact of complex innovation systems scales with the size of the system," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 141-151, July.
    16. Pablo Dorta-González & Rafael Suárez-Vega & María Isabel Dorta-González, 2020. "Open access effect on uncitedness: a large-scale study controlling by discipline, source type and visibility," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2619-2644, September.

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