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Is academic writing becoming more positive? A large-scale diachronic case study of Science research articles across 25 years

Author

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  • Zhou-min Yuan

    (Nanjing University of Posts and Telecommunications)

  • Mingxin Yao

    (Nanjing University)

Abstract

Academic writing is developing to be more positive. This linguistic positivity bias is confirmed in academic writing across disciplines and genres. The current research adopted sentiment analysis and examined the diachronic change in linguistic positivity in the full texts of 2,556 research articles published in Science in 25 years. The results showed that academic writing in research articles in the journal Science has become significantly more positive in the past 25 years. The findings of this study confirm linguistic positivity bias in academic writing based on empirical data from Science. Reasons for the increasingly positive language use in science articles might include the popularization of science, the growing number of researchers, and the difficulty of publishing in high-impact journals. Finally, this study discussed the implications of our findings for researchers, editors, and peer reviewers.

Suggested Citation

  • Zhou-min Yuan & Mingxin Yao, 2022. "Is academic writing becoming more positive? A large-scale diachronic case study of Science research articles across 25 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6191-6207, November.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:11:d:10.1007_s11192-022-04515-2
    DOI: 10.1007/s11192-022-04515-2
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    References listed on IDEAS

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    Cited by:

    1. Mingxin Yao & Ying Wei & Huiyu Wang, 2023. "Promoting research by reducing uncertainty in academic writing: a large-scale diachronic case study on hedging in Science research articles across 25 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4541-4558, August.
    2. Yongchao Ma & Ying Teng & Zhongzhun Deng & Li Liu & Yi Zhang, 2023. "Does writing style affect gender differences in the research performance of articles?: An empirical study of BERT-based textual sentiment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2105-2143, April.
    3. Sun, Zhuanlan & Clark Cao, C. & Ma, Chao & Li, Yiwei, 2023. "The academic status of reviewers predicts their language use," Journal of Informetrics, Elsevier, vol. 17(4).
    4. Sun, Zhuanlan, 2024. "Textual features of peer review predict top-cited papers: An interpretable machine learning perspective," Journal of Informetrics, Elsevier, vol. 18(2).
    5. 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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