IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v12y2018i1p59-73.html
   My bibliography  Save this article

Characterizing in-text citations in scientific articles: A large-scale analysis

Author

Listed:
  • Boyack, Kevin W.
  • van Eck, Nees Jan
  • Colavizza, Giovanni
  • Waltman, Ludo

Abstract

We report characteristics of in-text citations in over five million full text articles from two large databases – the PubMed Central Open Access subset and Elsevier journals – as functions of time, textual progression, and scientific field. The purpose of this study is to understand the characteristics of in-text citations in a detailed way prior to pursuing other studies focused on answering more substantive research questions. As such, we have analyzed in-text citations in several ways and report many findings here. Perhaps most significantly, we find that there are large field-level differences that are reflected in position within the text, citation interval (or reference age), and citation counts of references. In general, the fields of Biomedical and Health Sciences, Life and Earth Sciences, and Physical Sciences and Engineering have similar reference distributions, although they vary in their specifics. The two remaining fields, Mathematics and Computer Science and Social Science and Humanities, have different reference distributions from the other three fields and between themselves. We also show that in all fields the numbers of sentences, references, and in-text mentions per article have increased over time, and that there are field-level and temporal differences in the numbers of in-text mentions per reference. A final finding is that references mentioned only once tend to be much more highly cited than those mentioned multiple times.

Suggested Citation

  • Boyack, Kevin W. & van Eck, Nees Jan & Colavizza, Giovanni & Waltman, Ludo, 2018. "Characterizing in-text citations in scientific articles: A large-scale analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 59-73.
  • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:59-73
    DOI: 10.1016/j.joi.2017.11.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157717303516
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2017.11.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ding, Ying & Liu, Xiaozhong & Guo, Chun & Cronin, Blaise, 2013. "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, Elsevier, vol. 7(3), pages 583-592.
    2. Marc Bertin & Iana Atanassova & Yves Gingras & Vincent Larivière, 2016. "The invariant distribution of references in scientific articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(1), pages 164-177, January.
    3. V. Cano, 1989. "Citation behavior: Classification, utility, and location," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(4), pages 284-290, July.
    4. Xiaojun Wan & Fang Liu, 2014. "Are all literature citations equally important? Automatic citation strength estimation and its applications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(9), pages 1929-1938, September.
    5. Susan Bonzi, 1982. "Characteristics of a Literature as Predictors of Relatedness Between Cited and Citing Works," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 33(4), pages 208-216, July.
    6. Hu, Zhigang & Chen, Chaomei & Liu, Zeyuan, 2013. "Where are citations located in the body of scientific articles? A study of the distributions of citation locations," Journal of Informetrics, Elsevier, vol. 7(4), pages 887-896.
    7. Richard Van Noorden & Brendan Maher & Regina Nuzzo, 2014. "The top 100 papers," Nature, Nature, vol. 514(7524), pages 550-553, October.
    8. Patricia A. Hooten, 1991. "Frequency and functional use of cited documents in information science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 42(6), pages 397-404, July.
    9. Xiaodan Zhu & Peter Turney & Daniel Lemire & André Vellino, 2015. "Measuring academic influence: Not all citations are equal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 408-427, February.
    10. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co-citation clustering using full text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
    11. Hu, Zhigang & Lin, Gege & Sun, Taian & Hou, Haiyan, 2017. "Understanding multiply mentioned references," Journal of Informetrics, Elsevier, vol. 11(4), pages 948-958.
    12. Lutz Bornmann & Hans‐Dieter Daniel, 2007. "Multiple publication on a single research study: Does it pay? The influence of number of research articles on total citation counts in biomedicine," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(8), pages 1100-1107, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dangzhi Zhao & Andreas Strotmann, 2020. "Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 255-270, July.
    2. Liyue Chen & Jielan Ding & Vincent Larivière, 2022. "Measuring the citation context of national self‐references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 671-686, May.
    3. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
    4. Dangzhi Zhao & Andreas Strotmann, 2020. "Deep and narrow impact: introducing location filtered citation counting," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 503-517, January.
    5. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    6. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.
    7. Hamid R. Jamali & Majid Nabavi & Saeid Asadi, 2018. "How video articles are cited, the case of JoVE: Journal of Visualized Experiments," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1821-1839, December.
    8. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.
    9. Weibin Wang & Zheng Wang & Tian Yu & CholMyong Pak & Guang Yu, 2020. "Research on citation mention times and contributions using a neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2383-2400, December.
    10. Chao Lu & Ying Ding & Chengzhi Zhang, 2017. "Understanding the impact change of a highly cited article: a content-based citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 927-945, August.
    11. Dongqing Lyu & Xuanmin Ruan & Juan Xie & Ying Cheng, 2021. "The classification of citing motivations: a meta-synthesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3243-3264, April.
    12. CholMyong Pak & Guang Yu & Weibin Wang, 2018. "A study on the citation situation within the citing paper: citation distribution of references according to mention frequency," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 905-918, March.
    13. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    14. Drahomira Herrmannova & Robert M. Patton & Petr Knoth & Christopher G. Stahl, 2018. "Do citations and readership identify seminal publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 239-262, April.
    15. 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).
    16. Zehra Taşkın & Umut Al, 2018. "A content-based citation analysis study based on text categorization," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 335-357, January.
    17. Toluwase Victor Asubiaro & Isola Ajiferuke, 2022. "Semantic similarity-based credit attribution on citation paths: a method for allocating residual citation to and investigating depth of influence of scientific communications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6257-6277, November.
    18. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    19. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    20. Marc Bertin & Iana Atanassova & Cassidy R. Sugimoto & Vincent Lariviere, 2016. "The linguistic patterns and rhetorical structure of citation context: an approach using n-grams," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1417-1434, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:12:y:2018:i:1:p:59-73. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.