Analyzing linguistic complexity and scientific impact. Issue 3 (August 2019)

• Record Type: Journal Article
• Authors: Lu, Chao; Bu, Yi; Dong, Xianlei; Wang, Jie; Ding, Ying; Larivière, Vincent; Sugimoto, Cassidy R.; Paul, Logan; Zhang, Chengzhi
• Is Part Of: Journal of informetrics. Volume 13:Issue 3(2019)
• Page Start: 817
• Page End: 829
• Publication Date: 2019-08
• Subjects: English scientific writing -- Syntactic complexity -- Lexical complexity -- Lexical diversity -- Lexical density -- Lexical sophistication; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2019.07.004
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: This paper studies the relationship between the linguistic complexity and the scientific impact of publications. High-impact articles usually demonstrate a moderate linguistic complexity, while low-impact ones exhibit random linguistic patterns. Point estimation and confidence intervals suggest that these trends are not practical significant. Abstract: The number of publications and the number of citations received have become the most common indicators of scholarly success. In this context, scientific writing increasingly plays an important role in scholars' scientific careers. To understand the relationship between scientific writing and scientific impact, this paper selected 12 variables of linguistic complexity as a proxy for depicting scientific writing. We then analyzed these features from 36, 400 full-text Biology articles and 1, 797 full-text Psychology articles. These features were compared to the scientific impact of articles, grouped into high, medium, and low categories. The results suggested no practical significant relationship between linguistic complexity and citation strata in either discipline. This suggests that textual complexity plays little role in scientific impact in our data sets.