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Characterizing references from different disciplines: A perspective of citation content analysis

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  • Zhang, Chengzhi
  • Liu, Lifan
  • Wang, Yuzhuo

Abstract

Multidisciplinary cooperation is now common in research since social issues inevitably involve multiple disciplines. In research articles, reference information, especially citation content, is an important representation of communication among different disciplines. Analyzing the distribution characteristics of references from different disciplines in research articles is basic to detecting the sources of referred information and identifying contributions of different disciplines. This work takes articles in PLoS as the data and characterizes the references from different disciplines based on Citation Content Analysis (CCA). First, we download 210,334 full-text articles from PLoS and collect the information of the in-text citations. Then, we identify the discipline of each reference in these academic articles. To characterize the distribution of these references, we analyze three characteristics, namely, the number of citations, the average cited intensity and the average citation length. Finally, we conclude that the distributions of references from different disciplines are significantly different. Although most references come from Natural Science, Humanities and Social Sciences play important roles in the Introduction and Background sections of the articles. Basic disciplines, such as Mathematics, mainly provide research methods in the articles in PLoS. Citations mentioned in the Results and Discussion sections of articles are mainly in-discipline citations, such as citations from Nursing and Medicine in PLoS.

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  • Zhang, Chengzhi & Liu, Lifan & Wang, Yuzhuo, 2021. "Characterizing references from different disciplines: A perspective of citation content analysis," Journal of Informetrics, Elsevier, vol. 15(2).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000055
    DOI: 10.1016/j.joi.2021.101134
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    References listed on IDEAS

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    5. Liu, Jialin & Chen, Hongkan & Liu, Zhibo & Bu, Yi & Gu, Weiye, 2022. "Non-linearity between referencing behavior and citation impact: A large-scale, discipline-level analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    6. Jinqing Yang & Zhifeng Liu & Xiufeng Cheng & Guanghui Ye, 2024. "Understanding the keyword adoption behavior patterns of researchers from a functional structure perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3359-3384, June.
    7. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).

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