Emerging research topics detection with multiple machine learning models

Shuo Xu, Liyuan Hao, Xin An, Guancan Yang and Feifei Wang

Journal of Informetrics, 2019, vol. 13, issue 4

Abstract: Emerging research topic detection can benefit the research foundations and policy-makers. With the long-term and recent interest in detecting emerging research topics, various approaches are proposed in the literature. Though, there is still a lack of well-established linkages between the clear conceptual definition of emerging research topics and the proposed indicators for operationalization. This work follows the definition by Wang (2018), and several machine learning models are together used to detect and foresight the emerging research topics. Finally, experimental results on gene editing dataset discover three emerging research topics, which make clear that it is feasible to identify emerging research topics with our framework.

Keywords: Emerging research topics; Topic modeling; Dynamic Influence Model; Citation Influence Model; Machine learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:13:y:2019:i:4:s1751157719300367

DOI: 10.1016/j.joi.2019.100983

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