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RefCit2vec: embedding models considering references and citations for measuring document similarity

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Abstract

This study outlines the intellectual structure of Library and Information Science in terms of the venues with RefCit2vec, an embedding method inspired by word2vec. The reference lists or cited-by lists of 62,077 articles in 35 venues (journals and proceedings) between 1928 and 2022 are converted into real number vectors by four independent models of RefCit2vec. The document similarities measured by the two models of RefCit2vec exhibit moderate correlations with bibliographical coupling metrics. In contrast, the similarities from the other two models moderately or strongly correlate with co-citation metrics. Each venue is represented by its centroid, the average vector of its constituent documents. By applying hierarchical agglomerative clustering on the venue centroids, 69% of venues robustly emerge in 6 out of 8 clusters. Four clusters consistently form the library-related branch. The bibliometrics/scientometrics branch contains only 1 cluster, whereas the information-related branch contains 3 clusters. 43% of venues are in six subgroups of consistent tree structures. An article is defined as SCIM-alike for it is closer to the SCIM centroid than half of SCIM articles are. 10% of JASIST articles are SCIM-alike upon their reference lists, and 5% of JASIST articles are SCIM-alike in terms of their cited-by lists. The percentage of SCIM-alike articles in JASIST hiked above the average between 2008 and 2018 but has dropped below the average since 2019. As we demonstrate the dynamics in LIS, citation embedding methods like RefCit2vec can incorporate citation-based, text-based, or authorship features to contribute to varied scenarios in investigating or exploring research fronts and scientific knowledge transfer.

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Funding

This work was partially supported by National Science and Technology Council of the Republic of China (Grant No. MOST 110-2410-H-002-232-MY2).

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Authors and Affiliations

  1. Department of Library and Information Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan, ROC

    Chien-chih Huang & Kuang-hua Chen

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  1. Chien-chih Huang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Kuang-hua Chen and Chien-chih Huang. The first draft of the manuscript was written by Chien-chih Huang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Kuang-hua Chen.

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Huang, Cc., Chen, Kh. RefCit2vec: embedding models considering references and citations for measuring document similarity. Scientometrics 129, 4669–4693 (2024). https://doi.org/10.1007/s11192-024-05067-3

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