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A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains

Author

Listed:
  • Nina Smirnova

    (GESIS - Leibniz Institute for the Social Sciences)

  • Philipp Mayr

    (GESIS - Leibniz Institute for the Social Sciences)

Abstract

Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher’s collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection. Record types “article” and “review” from four different scientific domains, namely social sciences, economics, oceanography and computer science, published from 2014 to 2019 in a scientific journal in English were considered. Six types of acknowledged entities, i.e., funding agency, grant number, individuals, university, corporation and miscellaneous, were extracted from the acknowledgement texts using a named entity recognition tagger and subsequently examined. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete. The analysis of the automatically extracted entities revealed differences and distinct patterns in the distribution of acknowledged entities of different types between different scientific domains. A strong association was found between acknowledged entity and scientific domain, and acknowledged entity and entity type. Only negligible correlation was found between the number of citations and the number of acknowledged entities. Generally, the number of words in the acknowledgement texts positively correlates with the number of acknowledged funding organizations, universities, individuals and miscellaneous entities. At the same time, acknowledgement texts with the larger number of sentences have more acknowledged individuals and miscellaneous categories.

Suggested Citation

  • Nina Smirnova & Philipp Mayr, 2023. "A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 709-734, January.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:1:d:10.1007_s11192-022-04554-9
    DOI: 10.1007/s11192-022-04554-9
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    References listed on IDEAS

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    1. Cristian Mejia & Yuya Kajikawa, 2018. "Using acknowledgement data to characterize funding organizations by the types of research sponsored: the case of robotics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 883-904, March.
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    4. Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    5. Katherine W. McCain, 2018. "Beyond Garfield’s Citation Index: an assessment of some issues in building a personal name Acknowledgments Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 605-631, February.
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    9. Adèle Paul-Hus & Adrián A Díaz-Faes & Maxime Sainte-Marie & Nadine Desrochers & Rodrigo Costas & Vincent Larivière, 2017. "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-14, October.
    10. Adèle Paul-Hus & Nadine Desrochers, 2019. "Acknowledgements are not just thank you notes: A qualitative analysis of acknowledgements content in scientific articles and reviews published in 2015," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
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