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Exploring linguistic characteristics of highly browsed and downloaded academic articles

Author

Listed:
  • Bikun Chen

    (Nanjing University of Science and Technology)

  • Dannan Deng

    (Nanjing University of Science and Technology)

  • Zhouyan Zhong

    (Nanjing University of Science and Technology)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

Abstract

Views and downloads of academic articles have become important supplementary indicators of scholarly impact. It is assumed that linguistic characteristics have an influence on article views and downloads to some extent. To understand the relationship between linguistic characteristics and article views and downloads, this study selected 63,002 full-text articles published from 2014 to 2015 in the PLoS (Public Library of Science) journals (PLoS Biology, PLoS Computational Biology, PLoS Genetics, PLoS Medicine, PLoS Neglected Tropical Diseases, PLoS One and PLoS Pathogens), and introduced seven indicators (title length, abstract length, full text length, sentence length, lexical diversity, lexical density and lexical sophistication) to measure linguistic characteristics of articles, grouped into Top 20% viewed and downloaded (proxy of highly browsed and downloaded articles), total and Bottom 20% viewed and downloaded categories. The results suggested that most linguistic characteristics played little role in article views and downloads in our data sets in general, but some linguistic characteristics (e.g. title length and average sentence length) in specific PLoS journal and platform (PLoS platform or PubMed Central platform) played certain role in article views and downloads. Also, journal differences and platform differences regarding linguistic characteristics of highly viewed and downloaded articles were existed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:122:y:2020:i:3:d:10.1007_s11192-020-03361-4
    DOI: 10.1007/s11192-020-03361-4
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    Cited by:

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    2. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. ↓Xia Peng & Zequan Xiong & Li Yang, 2024. "Can document characteristics affect motivations for literature usage?," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3543-3563, June.
    8. 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.

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    More about this item

    Keywords

    Linguistic characteristics; Linguistic complexity; Usage metrics; PLoS; PubMed Central;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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