Decoding the writing styles of disciplines: A large-scale quantitative analysis

Abstract

Disciplinary writing style stems from the practice of science, reflecting the scientific culture. This study aims to explore the differences and evolution of scientific writing styles from the perspective of disciplines. A large-scale quantitative analysis was conducted over 14 million abstracts from the Microsoft Academic Graph (MAG) database across eight soft and hard disciplines. Represented by a comprehensive set of 14 symbolic, lexical, syntactic, structural, and readability features, the evolution of disciplinary writing styles was analyzed over 30 years. Interpretable machine learning methods were performed to test the discernibility of writing styles across disciplines and disclose their linguistic differences. Our findings reveal the linguistic features of soft disciplines (Art, Philosophy, and Sociology) and Mathematics generally keep stabilized, and a general trend of increasing linguistic complexity was observed for Biology, Chemistry, Computer Science, and Psychology. The good performance of the pairwise writing style classifiers indicates a well discriminability of the writing styles between disciplines. A correlation between the performance of classifiers and the distance between disciplines was identified. The feature contribution analysis using SHapley Additive exPlanations (SHAP) and Kendall's Tau rank correlation revealed the detailed commonalities and disparities in disciplines’ linguistic features. This study provides profound insights into the understanding of scientific writing and norms, which further helps develop useful tools for academic text analysis, foster interdisciplinary communication, and assist educators to construct discipline-specific writing guidance.