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Exploring linguistic characteristics of highly browsed and downloaded academic articles

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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.

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Similar content being viewed by others

Notes

  1. https://www.plos.org/article-level-metrics.

  2. http://www.lagotto.io/plos/.

  3. http://images.webofknowledge.com/WOKRS519B3/help/WOK/hp_usage_score.html.

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Acknowledgements

This paper is supported by Youth Program of National Social Science Fund in China (15CTQ035), Social Public Safety S&T Collaborative Innovation Center of Universities in Jiangsu Province and China Scholarship Council (ID: 201906845042).

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Authors and Affiliations

  1. Department of Information Management, Nanjing University of Science and Technology, Nanjing, 210094, China

    Bikun Chen, Dannan Deng, Zhouyan Zhong & Chengzhi Zhang

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  1. Bikun Chen
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  2. Dannan Deng
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Correspondence to Bikun Chen.

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Chen, B., Deng, D., Zhong, Z. et al. Exploring linguistic characteristics of highly browsed and downloaded academic articles. Scientometrics 122, 1769–1790 (2020). https://doi.org/10.1007/s11192-020-03361-4

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