IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v128y2023i1d10.1007_s11192-022-04550-z.html
   My bibliography  Save this article

Linguistic complexity in scientific writing: A large-scale diachronic study from 1821 to 1920

Author

Listed:
  • Gui Wang

    (Shanghai Normal University)

  • Hui Wang

    (Shanghai Normal University)

  • Xinyi Sun

    (Shanghai Normal University)

  • Nan Wang

    (Beijing Foreign Studies University)

  • Li Wang

    (Shanghai Normal University)

Abstract

This study intends to describe the diachronic changes of linguistic complexity (i.e., overall, morphological, and syntactic complexity) in scientific writing based on Kolmogorov complexity, an information-theoretic approach. We have chosen the entire data (i.e., all the 24 text types including articles, letters, news, etc.) and two individual registers (i.e., the full texts and abstracts of articles) of Philosophical Transactions of the Royal Society of London, the world’s oldest scientific writing journal. The Mann–Kendall trend tests were used to capture diachronic changes in linguistic complexity at three complexity levels, and the Pearson correlation coefficients were calculated to investigate the relationships between the three complexity metrics. Results showed that the overall and morphological complexity of both the entire data and full texts increased from 1821 to 1920, indicating a massive lexical expansion during this 100-year period, as evidenced by more and more word form variants in scientific writing. In contrast, the syntactic complexity of the entire data and full texts declined, suggesting a gradual shift towards grammatical simplification in the evolution of scientific writing, particularly in word order rules and syntactic patterns. A trade-off effect has also been found between syntactic and morphological complexity in the entire data. In addition, concerning abstracts, the overall and morphological complexity decreased while the syntactic complexity increased. Drawing from these results, researchers can better understand the changing linguistic complexity styles in scientific writing, thus making adjustments in their writing accordingly to garner greater attention in academia.

Suggested Citation

  • Gui Wang & Hui Wang & Xinyi Sun & Nan Wang & Li Wang, 2023. "Linguistic complexity in scientific writing: A large-scale diachronic study from 1821 to 1920," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 441-460, January.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:1:d:10.1007_s11192-022-04550-z
    DOI: 10.1007/s11192-022-04550-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04550-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-022-04550-z?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. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    2. Lu, Chao & Bu, Yi & Dong, Xianlei & Wang, Jie & Ding, Ying & Larivière, Vincent & Sugimoto, Cassidy R. & Paul, Logan & Zhang, Chengzhi, 2019. "Analyzing linguistic complexity and scientific impact," Journal of Informetrics, Elsevier, vol. 13(3), pages 817-829.
    3. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    4. Chao Lu & Yi Bu & Jie Wang & Ying Ding & Vetle Torvik & Matthew Schnaars & Chengzhi Zhang, 2019. "Examining scientific writing styles from the perspective of linguistic complexity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(5), pages 462-475, May.
    Full references (including those not matched with items on IDEAS)

    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. Song, Ningyuan & Chen, Kejun & Zhao, Yuehua, 2023. "Understanding writing styles of scientific papers in the IS-LS domain: Evidence from abstracts over the past three decades," Journal of Informetrics, Elsevier, vol. 17(1).
    2. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    3. Don Watson & Manfred Krug & Claus-Christian Carbon, 2022. "The relationship between citations and the linguistic traits of specific academic discourse communities identified by using social network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1755-1781, April.
    4. Brito, Ana C.M. & Silva, Filipi N. & de Arruda, Henrique F. & Comin, Cesar H. & Amancio, Diego R. & Costa, Luciano da F., 2021. "Classification of abrupt changes along viewing profiles of scientific articles," Journal of Informetrics, Elsevier, vol. 15(2).
    5. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    6. van den Besselaar, Peter & Mom, Charlie, 2022. "The effect of writing style on success in grant applications," Journal of Informetrics, Elsevier, vol. 16(1).
    7. Xueying Liu & Haoran Zhu, 2023. "Linguistic positivity in soft and hard disciplines: temporal dynamics, disciplinary variation, and the relationship with research impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3107-3127, May.
    8. Tan Jin & Huiqiong Duan & Xiaofei Lu & Jing Ni & Kai Guo, 2021. "Do research articles with more readable abstracts receive higher online attention? Evidence from Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8471-8490, October.
    9. Amon, Julian & Hornik, Kurt, 2022. "Is it all bafflegab? – Linguistic and meta characteristics of research articles in prestigious economics journals," Journal of Informetrics, Elsevier, vol. 16(2).
    10. Kun Sun & Rong Wang, 2022. "The Evolutionary Pattern of Language in English Fiction Over the Last Two Centuries: Insights From Linguistic Concreteness and Imageability," SAGE Open, , vol. 12(1), pages 21582440211, January.
    11. Guo Chen & Jing Chen & Yu Shao & Lu Xiao, 2023. "Automatic noise reduction of domain-specific bibliographic datasets using positive-unlabeled learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1187-1204, February.
    12. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
    13. Charles J. Gomez & Andrew C. Herman & Paolo Parigi, 2022. "Leading countries in global science increasingly receive more citations than other countries doing similar research," Nature Human Behaviour, Nature, vol. 6(7), pages 919-929, July.
    14. Lu, Wei & Liu, Zhifeng & Huang, Yong & Bu, Yi & Li, Xin & Cheng, Qikai, 2020. "How do authors select keywords? A preliminary study of author keyword selection behavior," Journal of Informetrics, Elsevier, vol. 14(4).
    15. Diego Marino Fages, 2020. "Write better, publish better," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1671-1681, March.
    16. Ante, Lennart, 2022. "The relationship between readability and scientific impact: Evidence from emerging technology discourses," Journal of Informetrics, Elsevier, vol. 16(1).
    17. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    18. Shuo Xu & Ling Li & Xin An & Liyuan Hao & Guancan Yang, 2021. "An approach for detecting the commonality and specialty between scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7445-7475, September.
    19. Lu, Chao & Bu, Yi & Dong, Xianlei & Wang, Jie & Ding, Ying & Larivière, Vincent & Sugimoto, Cassidy R. & Paul, Logan & Zhang, Chengzhi, 2019. "Analyzing linguistic complexity and scientific impact," Journal of Informetrics, Elsevier, vol. 13(3), pages 817-829.
    20. Nhu Le Quynh Nguy & Hung Tan Ha, 2022. "Lexical Profile of Academic Written English Revisited: What Does it Take to Understand Scholarly Abstracts?," SAGE Open, , vol. 12(3), pages 21582440221, September.

    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:spr:scient:v:128:y:2023:i:1:d:10.1007_s11192-022-04550-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.