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Using the appearance of citations in full text on author co-citation analysis

Author

Listed:
  • Yi Bu

    (Indiana University)

  • Binglu Wang

    (Peking University)

  • Win-bin Huang

    (Peking University)

  • Shangkun Che

    (Peking University)

  • Yong Huang

    (Wuhan University)

Abstract

As a frequently used method of depicting scientific intellectual structures, author co-citation analysis (ACA) has been applied to many domains. However, only count-based information is involved as the input of ACA, which is not sufficiently informative for knowledge representations. This article catches several metadata in full text of citing papers but not aims at content-level information, which increases the amount of information input to ACA without increasing computational complexity a lot. We propose a new method by involving information including the number of mentioned times in a citing paper and the number of context words in a citing sentence. We combine these pieces of information into the traditional ACA and compare the results between ACA and the proposed approach by using factor analysis, network analysis, and MDS-measurement. The result of our empirical study indicates that compared with the traditional ACA, the proposed method shows a better clustering performance in visualizations and reveals more details in displaying intellectual structures.

Suggested Citation

  • Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:1:d:10.1007_s11192-018-2757-z
    DOI: 10.1007/s11192-018-2757-z
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    References listed on IDEAS

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    Cited by:

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    3. Nayak, Bishwajit & Bhattacharyya, Som Sekhar & Krishnamoorthy, Bala, 2022. "Exploring the black box of competitive advantage – An integrated bibliometric and chronological literature review approach," Journal of Business Research, Elsevier, vol. 139(C), pages 964-982.
    4. 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.
    5. Ruhao Zhang & Junpeng Yuan, 2022. "Enhanced author bibliographic coupling analysis using semantic and syntactic citation information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7681-7706, December.
    6. Mohammad Sahabuddin & Md. Nazmus Sakib & Md. Mahbubur Rahman & Adamu Jibir & Mochammad Fahlevi & Mohammed Aljuaid & Sandra Grabowska, 2023. "The Evolution of FinTech in Scientific Research: A Bibliometric Analysis," Sustainability, MDPI, vol. 15(9), pages 1-16, April.
    7. Paúl Carrión-Mero & Néstor Montalván-Burbano & Fernando Morante-Carballo & Adolfo Quesada-Román & Boris Apolo-Masache, 2021. "Worldwide Research Trends in Landslide Science," IJERPH, MDPI, vol. 18(18), pages 1-24, September.

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    More about this item

    Keywords

    Author co-citation analysis; Co-citation analysis; Citation analysis; Bibliometrics; Scientometrics; Mapping knowledge domains;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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