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Mixed-indicators model for identifying emerging research areas

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Abstract

This study presents a mixed model that combines different indicators to describe and predict key structural and dynamic features of emerging research areas. Three indicators are combined: sudden increases in the frequency of specific words; the number and speed by which new authors are attracted to an emerging research area, and changes in the interdisciplinarity of cited references. The mixed model is applied to four emerging research areas: RNAi, Nano, h-Index, and Impact Factor research using papers published in the Proceedings of the National Academy of Sciences of the United States of America (1982–2009) and in Scientometrics (1978–2009). Results are compared in terms of strengths and temporal dynamics. Results show that the indicators are indicative of emerging areas and they exhibit interesting temporal correlations: new authors enter the area first, then the interdisciplinarity of paper references increases, then word bursts occur. All workflows are reported in a manner that supports replication and extension by others.

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Acknowledgments

We would like to thank Joseph Biberstine and Russell J. Duhon for developing custom queries and code and appreciate the expert comments from the three anonymous reviewers. This work is funded by the James S. McDonnell Foundation and the National Institutes of Health under awards R21DA024259 and U24RR029822.

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Authors and Affiliations

  1. WISE Lab, Dalian University of Technology, Dalian, China

    Hanning Guo

  2. Cyberinfrastructure for Network Science Center, School of Library and Information Science, Indiana University, Bloomington, IN, USA

    Hanning Guo, Scott Weingart & Katy Börner

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  1. Hanning Guo
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  2. Scott Weingart
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  3. Katy Börner
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Correspondence to Hanning Guo.

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Guo, H., Weingart, S. & Börner, K. Mixed-indicators model for identifying emerging research areas. Scientometrics 89, 421–435 (2011). https://doi.org/10.1007/s11192-011-0433-7

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