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Content- and proximity-based author co-citation analysis using citation sentences

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  • Kim, Ha Jin
  • Jeong, Yoo Kyung
  • Song, Min

Abstract

Author co-citation analysis (ACA) has been widely used for identifying the subject disciplines of authors. Citations can reveal the explicit relationship between authors as well as their subject research fields. However, previous studies have seldom considered citation contents that convey useful implicit information on the authors or the influence of the links between the authors’ subject fields by taking citation locations into account. This study aims to reveal the implicit relationship in the authors’ subject disciplines by considering both citation contents and proximity. To this end, the researchers propose a new ACA method, called content- and proximity-based author co-citation analysis (CPACA). For the study, we extracted citation sentences and locations from full-text articles in the oncology field. The top 15 journals on oncology in Journal Citation Reports were selected, and 6,360 full-text articles from PubMed Central were collected. The results show that the proposed method enables the identification of distinct sub-fields of authors to represent authors’ subject relatedness.

Suggested Citation

  • Kim, Ha Jin & Jeong, Yoo Kyung & Song, Min, 2016. "Content- and proximity-based author co-citation analysis using citation sentences," Journal of Informetrics, Elsevier, vol. 10(4), pages 954-966.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:954-966
    DOI: 10.1016/j.joi.2016.07.007
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    References listed on IDEAS

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    Cited by:

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    3. 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.
    4. 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.
    5. 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.
    6. Naveed Naeem Abbas & Tanveer Ahmed & Syed Habib Ullah Shah & Muhammad Omar & Han Woo Park, 2019. "Investigating the applications of artificial intelligence in cyber security," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 1189-1211, November.
    7. Oscar Rodriguez-Prieto & Lourdes Araujo & Juan Martinez-Romo, 2019. "Discovering related scientific literature beyond semantic similarity: a new co-citation approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 105-127, July.
    8. 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.
    9. Fernando Morante-Carballo & Néstor Montalván-Burbano & Maribel Aguilar-Aguilar & Paúl Carrión-Mero, 2022. "A Bibliometric Analysis of the Scientific Research on Artisanal and Small-Scale Mining," IJERPH, MDPI, vol. 19(13), pages 1-29, July.
    10. 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.
    11. Bu, Yi & Ni, Shaokang & Huang, Win-bin, 2017. "Combining multiple scholarly relationships with author cocitation analysis: A preliminary exploration on improving knowledge domain mappings," Journal of Informetrics, Elsevier, vol. 11(3), pages 810-822.

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