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Emerging research topics detection with multiple machine learning models

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

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.

Suggested Citation

  • Xu, Shuo & Hao, Liyuan & An, Xin & Yang, Guancan & Wang, Feifei, 2019. "Emerging research topics detection with multiple machine learning models," Journal of Informetrics, Elsevier, vol. 13(4).
    • Handle: RePEc:eee:infome:v:13:y:2019:i:4:s1751157719300367
    DOI: 10.1016/j.joi.2019.100983
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    References listed on IDEAS

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    7. Nhu Khoa Nguyen & Thierry Delahaut & Emanuela Boros & Antoine Doucet & Gael Lejeune, 2023. "Contextualizing Emerging Trends in Financial News Articles," Papers 2301.11318, arXiv.org.
    8. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
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