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Full‐text citation analysis: A new method to enhance scholarly networks

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  • Xiaozhong Liu
  • Jinsong Zhang
  • Chun Guo

Abstract

In this article, we use innovative full‐text citation analysis along with supervised topic modeling and network‐analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full‐text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author‐contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be “important publications” for the target topic (keyword), and we use these cited publications to validate our topic‐ranking result and to compare different publication‐ranking lists. Evaluation results show that full‐text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency–inverted document frequency (tf–idf), language model, BM25, PageRank, and PageRank + language model (p

Suggested Citation

  • Xiaozhong Liu & Jinsong Zhang & Chun Guo, 2013. "Full‐text citation analysis: A new method to enhance scholarly networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(9), pages 1852-1863, September.
  • Handle: RePEc:bla:jamist:v:64:y:2013:i:9:p:1852-1863
    DOI: 10.1002/asi.22883
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    Citations

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    Cited by:

    1. Luis Quevedo & Víctor Velasco & José à lvarez & Paula Moreno, 2023. "Mapping Tourism and Global Change: A Bibliometric Analysis (2012-2022)," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 12, March.
    2. Jensen, Scott & Liu, Xiaozhong & Yu, Yingying & Milojevic, Staša, 2016. "Generation of topic evolution trees from heterogeneous bibliographic networks," Journal of Informetrics, Elsevier, vol. 10(2), pages 606-621.
    3. 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.
    4. Matthias Sebastian Rüdiger & David Antons & Torsten-Oliver Salge, 2021. "The explanatory power of citations: a new approach to unpacking impact in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9779-9809, December.
    5. Rey-Long Liu, 2017. "A new bibliographic coupling measure with descriptive capability," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 915-935, February.
    6. Hindah Mustika & Anis Eliyana & Tri Siwi Agustina & Aisha Anwar, 2022. "Testing the Determining Factors of Knowledge Sharing Behavior," SAGE Open, , vol. 12(1), pages 21582440221, February.
    7. 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.
    8. 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.
    9. Lucia Saraswati & Tuty Anggraini & Fauzan Azima, 2023. "A Bibliometric Analysis of Trends in Food Safety Research: The Case of Chili Sauce," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(9), pages 27-32, September.
    10. Teng, Hao & Wang, Nan & Zhao, Hongyu & Hu, Yingtong & Jin, Haitao, 2024. "Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents," Journal of Informetrics, Elsevier, vol. 18(1).

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