Mixed-indicators model for identifying emerging research areas

Hanning Guo (), Scott Weingart () and Katy Börner ()
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Hanning Guo: Dalian University of Technology
Scott Weingart: School of Library and Information Science, Indiana University
Katy Börner: School of Library and Information Science, Indiana University

Scientometrics, 2011, vol. 89, issue 1, No 26, 435 pages

Abstract: 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.

Keywords: Burst detection; Prediction; Emerging trend; Temporal dynamics; Science of science (Sci2) tool (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (38)

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DOI: 10.1007/s11192-011-0433-7

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