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Improving the accuracy of co‐citation clustering using full text

Author

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  • Kevin W. Boyack
  • Henry Small
  • Richard Klavans
Abstract
Historically, co‐citation models have been based only on bibliographic information. Full‐text analysis offers the opportunity to significantly improve the quality of the signals upon which these co‐citation models are based. In this work we study the effect of reference proximity on the accuracy of co‐citation clusters. Using a corpus of 270,521 full text documents from 2007, we compare the results of traditional co‐citation clustering using only the bibliographic information to results from co‐citation clustering where proximity between reference pairs is factored into the pairwise relationships. We find that accounting for reference proximity from full text can increase the textual coherence (a measure of accuracy) of a co‐citation cluster solution by up to 30% over the traditional approach based on bibliographic information.

Suggested Citation

  • Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co‐citation clustering using full text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
    • Handle: RePEc:bla:jamist:v:64:y:2013:i:9:p:1759-1767
    DOI: 10.1002/asi.22896
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    Citations

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    Cited by:

    1. Raja Habib & Muhammad Tanvir Afzal, 2019. "Sections-based bibliographic coupling for research paper recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 643-656, May.
    2. Kamal Sanguri & Atanu Bhuyan & Sabyasachi Patra, 2020. "A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 233-269, October.
    3. Mengyu Yu & Mazie Krehbiel & Samantha Thompson & Tatjana Miljkovic, 2020. "An exploration of gender gap using advanced data science tools: actuarial research community," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 767-789, May.
    4. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    5. Yun, Jinhyuk, 2022. "Generalization of bibliographic coupling and co-citation using the node split network," Journal of Informetrics, Elsevier, vol. 16(2).
    6. Svitlana Petrasova & Nina Khairova & Włodzimierz Lewoniewski & Orken Mamyrbayev & Kuralay Mukhsina, 2018. "Similar Text Fragments Extraction for Identifying Common Wikipedia Communities," Data, MDPI, vol. 3(4), pages 1-9, December.
    7. 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.
    8. Li Zhang & Ming Liu & Bo Wang & Bo Lang & Peng Yang, 2021. "Discovering communities based on mention distance," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1945-1967, March.
    9. 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.
    10. 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.
    11. Riaz Ahmad & Muhammad Tanvir Afzal, 2018. "CAD: an algorithm for citation-anchors detection in research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1405-1423, December.

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