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How to normalize Twitter counts? A first attempt based on journals in the Twitter Index

Author

Listed:
  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

  • Robin Haunschild

    (Max Planck Institute for Solid State Research)

Abstract

One possible way of measuring the broad impact of research (societal impact) quantitatively is the use of alternative metrics (altmetrics). An important source of altmetrics is Twitter, which is a popular microblogging service. In bibliometrics, it is standard to normalize citations for cross-field comparisons. This study deals with the normalization of Twitter counts (TC). The problem with Twitter data is that many papers receive zero tweets or only one tweet. In order to restrict the impact analysis on only those journals producing a considerable Twitter impact, we defined the Twitter Index (TI) containing journals with at least 80 % of the papers with at least 1 tweet each. For all papers in each TI journal, we calculated normalized Twitter percentiles (TP) which range from 0 (no impact) to 100 (highest impact). Thus, the highest impact accounts for the paper with the most tweets compared to the other papers in the journal. TP are proposed to be used for cross-field comparisons. We studied the field-independency of TP in comparison with TC. The results point out that the TP can validly be used particularly in biomedical and health sciences, life and earth sciences, mathematics and computer science, as well as physical sciences and engineering. In a first application of TP, we calculated percentiles for countries. The results show that Denmark, Finland, and Norway are the countries with the most tweeted papers (measured by TP).

Suggested Citation

  • Lutz Bornmann & Robin Haunschild, 2016. "How to normalize Twitter counts? A first attempt based on journals in the Twitter Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1405-1422, June.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:3:d:10.1007_s11192-016-1893-6
    DOI: 10.1007/s11192-016-1893-6
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    References listed on IDEAS

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    13. 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.
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    17. Bornmann, Lutz & Haunschild, Robin, 2016. "Normalization of Mendeley reader impact on the reader- and paper-side: A comparison of the mean discipline normalized reader score (MDNRS) with the mean normalized reader score (MNRS) and bare reader ," Journal of Informetrics, Elsevier, vol. 10(3), pages 776-788.
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    Citations

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    Cited by:

    1. Robin Haunschild & Lutz Bornmann, 2018. "Field- and time-normalization of data with many zeros: an empirical analysis using citation and Twitter data," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 997-1012, August.
    2. Thelwall, Mike, 2017. "Three practical field normalised alternative indicator formulae for research evaluation," Journal of Informetrics, Elsevier, vol. 11(1), pages 128-151.
    3. Mi Kyung Lee & Ho Young Yoon & Marc Smith & Hye Jin Park & Han Woo Park, 2017. "Mapping a Twitter scholarly communication network: a case of the association of internet researchers’ conference," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 767-797, August.
    4. Robin Haunschild & Lutz Bornmann, 2017. "How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1209-1216, March.
    5. Saeed-Ul Hassan & Timothy D. Bowman & Mudassir Shabbir & Aqsa Akhtar & Mubashir Imran & Naif Radi Aljohani, 2019. "Influential tweeters in relation to highly cited articles in altmetric big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 481-493, April.
    6. Zhichao Fang & Rodrigo Costas & Paul Wouters, 2022. "User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4523-4546, August.
    7. Xuan Zhen Liu & Hui Fang, 2017. "What we can learn from tweets linking to research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 349-369, April.
    8. Bornmann, Lutz & Haunschild, Robin, 2018. "Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data," Journal of Informetrics, Elsevier, vol. 12(3), pages 998-1011.
    9. Ortega, José Luis, 2020. "Proposal of composed altmetric indicators based on prevalence and impact dimensions," Journal of Informetrics, Elsevier, vol. 14(4).
    10. Prabhsimran Singh & Yogesh K. Dwivedi & Karanjeet Singh Kahlon & Ravinder Singh Sawhney & Ali Abdallah Alalwan & Nripendra P. Rana, 0. "Smart Monitoring and Controlling of Government Policies Using Social Media and Cloud Computing," Information Systems Frontiers, Springer, vol. 0, pages 1-23.
    11. Liwei Zhang & Jue Wang, 2021. "What affects publications’ popularity on Twitter?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9185-9198, November.
    12. Prabhsimran Singh & Yogesh K. Dwivedi & Karanjeet Singh Kahlon & Ravinder Singh Sawhney & Ali Abdallah Alalwan & Nripendra P. Rana, 2020. "Smart Monitoring and Controlling of Government Policies Using Social Media and Cloud Computing," Information Systems Frontiers, Springer, vol. 22(2), pages 315-337, April.
    13. Manika Lamba, 2020. "Research productivity of health care policy faculty: a cohort study of Harvard Medical School," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 107-130, July.
    14. Dotti, Nicola Francesco & Walczyk, Julia, 2022. "What is the societal impact of university research? A policy-oriented review to map approaches, identify monitoring methods and success factors," Evaluation and Program Planning, Elsevier, vol. 95(C).

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