IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v16y2022i3s175115772200061x.html
   My bibliography  Save this article

Power Laws in altmetrics: An empirical analysis

Author

Listed:
  • Banshal, Sumit Kumar
  • Gupta, Solanki
  • Lathabai, Hiran H
  • Singh, Vivek Kumar

Abstract

Power Laws are a characteristic distribution found in both natural as well as in man-made systems. Previous studies have shown that citations to scientific articles follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. However, the distributional character of altmetrics (such as reads, likes, mentions, etc.) has not been studied in much detail, particularly with respect to existence of power law behaviours. This article, therefore, attempts to do an empirical analysis of altmetric mention data of a large set of scholarly articles to see if they exhibit power law. The individual and the composite data series of ‘mentions’ on the various platforms are fit to a power law distribution, and the parameters and goodness of fit are determined, both using least squares regression as well as the Maximum Likelihood Estimate (MLE) approach. We also explore the fit of the mention data to other distribution families like the Log-normal and exponential distributions. Results obtained confirm the existence of power law behaviour in social media mentions to scholarly articles. The Log-normal distribution also looks plausible but is not found to be statistically significant, and the exponential distribution does not show a good fit. Major implications of power law in altmetrics are given and interesting research questions are posed in pursuit of enhancing the reliability of altmetrics for research evaluation purposes.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:3:s175115772200061x
    DOI: 10.1016/j.joi.2022.101309
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S175115772200061X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2022.101309?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michal Brzezinski, 2015. "Power laws in citation distributions: evidence from Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 213-228, April.
    2. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    3. Hajar Sotudeh & Zahra Mazarei & Mahdieh Mirzabeigi, 2015. "CiteULike bookmarks are correlated to citations at journal and author levels in library and information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2237-2248, December.
    4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    5. Judit Bar-Ilan, 2014. "Astrophysics publications on arXiv, Scopus and Mendeley: a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 217-225, July.
    6. Kim Holmberg & Mike Thelwall, 2014. "Disciplinary differences in Twitter scholarly communication," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1027-1042, November.
    7. Mike Thelwall & Pardeep Sud, 2016. "Mendeley readership counts: An investigation of temporal and disciplinary differences," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(12), pages 3036-3050, December.
    8. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    9. Ye‐Sho Chen & Ferdinand F. Leimkuhler, 1986. "A relationship between Lotka's Law, Bradford's Law, and Zipf's Law," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 37(5), pages 307-314, September.
    10. Xavier Gabaix, 2016. "Power Laws in Economics: An Introduction," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 185-206, Winter.
    11. Kuang-hua Chen & Muh-chyun Tang & Chun-mei Wang & Jieh Hsiang, 2015. "Exploring alternative metrics of scholarly performance in the social sciences and humanities in Taiwan," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 97-112, January.
    12. Jeff Alstott & Ed Bullmore & Dietmar Plenz, 2014. "powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    13. Yufeng Duan & Zequan Xiong, 2017. "Download patterns of journal papers and their influencing factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1761-1775, September.
    14. Ehsan Mohammadi & Mike Thelwall & Kayvan Kousha, 2016. "Can Mendeley bookmarks reflect readership? A survey of user motivations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1198-1209, May.
    15. Javier Ruiz-Castillo, 2012. "The evaluation of citation distributions," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 3(1), pages 291-310, March.
    16. Xianwen Wang & Zhichao Fang & Xiaoling Sun, 2016. "Usage patterns of scholarly articles on Web of Science: a study on Web of Science usage count," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 917-926, November.
    17. Thelwall, Mike, 2016. "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression," Journal of Informetrics, Elsevier, vol. 10(2), pages 336-346.
    18. Mike Thelwall & Paul Wilson, 2016. "Mendeley readership altmetrics for medical articles: An analysis of 45 fields," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(8), pages 1962-1972, August.
    19. Thelwall, Mike & Nevill, Tamara, 2018. "Could scientists use Altmetric.com scores to predict longer term citation counts?," Journal of Informetrics, Elsevier, vol. 12(1), pages 237-248.
    20. Zohreh Zahedi & Rodrigo Costas & Paul Wouters, 2014. "How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1491-1513, November.
    21. Ehsan Mohammadi & Mike Thelwall, 2013. "Assessing non-standard article impact using F1000 labels," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 383-395, November.
    22. Mike Thelwall, 2018. "Early Mendeley readers correlate with later citation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1231-1240, June.
    23. Hadas Shema & Judit Bar-Ilan & Mike Thelwall, 2014. "Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(5), pages 1018-1027, May.
    24. 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.
    25. Stefanie Haustein & Isabella Peters & Cassidy R. Sugimoto & Mike Thelwall & Vincent Larivière, 2014. "Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 656-669, April.
    26. Rodrigo Costas & Zohreh Zahedi & Paul Wouters, 2015. "Do “altmetrics” correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(10), pages 2003-2019, October.
    27. Björn Hammarfelt, 2014. "Using altmetrics for assessing research impact in the humanities," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1419-1430, November.
    28. Sebastian Vogl & Thomas Scherndl & Anton Kühberger, 2018. "#Psychology: a bibliometric analysis of psychological literature in the online media," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1253-1269, June.
    29. Mike Thelwall & Kayvan Kousha, 2017. "ResearchGate articles: Age, discipline, audience size, and impact," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(2), pages 468-479, February.
    30. Thelwall, Mike, 2016. "Are the discretised lognormal and hooked power law distributions plausible for citation data?," Journal of Informetrics, Elsevier, vol. 10(2), pages 454-470.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Houqiang & Li, Longfei & Cao, Xueting & Chen, Tao, 2022. "Exploring country's preference over news mentions to academic papers," Journal of Informetrics, Elsevier, vol. 16(4).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Mousumi Karmakar & Sumit Kumar Banshal & Vivek Kumar Singh, 2020. "Does presence of social media plugins in a journal website result in higher social media attention of its research publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2103-2143, September.
    3. Zhiqi Wang & Wolfgang Glänzel & Yue Chen, 2020. "The impact of preprints in Library and Information Science: an analysis of citations, usage and social attention indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1403-1423, November.
    4. Thelwall, Mike & Nevill, Tamara, 2018. "Could scientists use Altmetric.com scores to predict longer term citation counts?," Journal of Informetrics, Elsevier, vol. 12(1), pages 237-248.
    5. Sergio Copiello, 2020. "Other than detecting impact in advance, alternative metrics could act as early warning signs of retractions: tentative findings of a study into the papers retracted by PLoS ONE," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2449-2469, December.
    6. Mike Thelwall, 2017. "Are Mendeley reader counts useful impact indicators in all fields?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1721-1731, December.
    7. Mike Thelwall, 2018. "Differences between journals and years in the proportions of students, researchers and faculty registering Mendeley articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 717-729, May.
    8. Mohammadamin Erfanmanesh & A. Noorhidawati & A. Abrizah, 2019. "What can Bookmetrix tell us about the impact of Springer Nature’s books," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 521-536, October.
    9. Amalia Mas-Bleda & Mike Thelwall, 2016. "Can alternative indicators overcome language biases in citation counts? A comparison of Spanish and UK research," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2007-2030, December.
    10. Maryam Moshtagh & Tahereh Jowkar & Maryam Yaghtin & Hajar Sotudeh, 2023. "The moderating effect of altmetrics on the correlations between single and multi-faceted university ranking systems: the case of THE and QS vs. Nature Index and Leiden," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 761-781, January.
    11. Mike Thelwall & Kayvan Kousha & Mahshid Abdoli & Emma Stuart & Meiko Makita & Paul Wilson & Jonathan Levitt, 2023. "Do altmetric scores reflect article quality? Evidence from the UK Research Excellence Framework 2021," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 582-593, May.
    12. Siluo Yang & Xin Xing & Fan Qi & Maria Cláudia Cabrini Grácio, 2021. "Comparison of academic book impact from a disciplinary perspective: an analysis of citations and altmetric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1101-1123, February.
    13. Bornmann, Lutz, 2014. "Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics," Journal of Informetrics, Elsevier, vol. 8(4), pages 895-903.
    14. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
    15. Saeed-Ul Hassan & Mubashir Imran & Uzair Gillani & Naif Radi Aljohani & Timothy D. Bowman & Fereshteh Didegah, 2017. "Measuring social media activity of scientific literature: an exhaustive comparison of scopus and novel altmetrics big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 1037-1057, November.
    16. Liwei Zhang & Jue Wang, 2018. "Why highly cited articles are not highly tweeted? A biology case," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 495-509, October.
    17. Mrowinski, Maciej J. & Gagolewski, Marek & Siudem, Grzegorz, 2022. "Accidentality in journal citation patterns," Journal of Informetrics, Elsevier, vol. 16(4).
    18. 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.
    19. Isidro F. Aguillo, 2020. "Altmetrics of the Open Access Institutional Repositories: a webometrics approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(3), pages 1181-1192, June.
    20. Yu Liu & Dan Lin & Xiujuan Xu & Shimin Shan & Quan Z. Sheng, 2018. "Multi-views on Nature Index of Chinese academic institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 823-837, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:16:y:2022:i:3:s175115772200061x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.