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Three new bibliometric indicators/approaches derived from keyword analysis

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Abstract

Keyword analysis has been an important research theme in bibliometrics. The deduction of new valuable bibliometric indicators/approaches through keyword analysis is important for prompting the further development of this subject area. In this study, the following three bibliometric indicators/approaches were thus derived. Indicator K was derived using the ratio between the average unique keyword number and average keyword frequency of a discipline for quantitatively describing the discipline’s development stages highlighted by scientific-philosopher Kuhn. Next, the correlation matrix analysis was used after k-core filtration to quantitatively expose the detailed correlations between topics for a large network. Thirdly, indicators I (node betweenness divided by node degree) and C (clustering coefficient) were collectively introduced to predict potential growth keywords. Diverse topical evolutions were categorized into a strategic diagram according to the tendencies of I and C. With sustainable development as a case study, we verified that the three new bibliometric indicators/approaches work well and can realize many new concepts beyond the scope of available indicators or approaches. In summary, the present paper makes a renewed effort to promote the development of bibliometrics. We hope our work could catalyze the further studies from the communities in the scientometric fields.

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Acknowledgements

We acknowledge the valuable guidelines on the revision and elaboration of the study from reviewers.

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Tianjin University, Tianjin, 300354, China

    Mengyang Wang & Lihe Chai

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  1. Mengyang Wang
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  2. Lihe Chai
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Correspondence to Mengyang Wang.

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Wang, M., Chai, L. Three new bibliometric indicators/approaches derived from keyword analysis. Scientometrics 116, 721–750 (2018). https://doi.org/10.1007/s11192-018-2768-9

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