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Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction

Author

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  • Feng Wu

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Wanqiang Xu

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chaoran Lin

    (College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
    School of Economics and Management, Harbin Engineering University, Harbin 150001, China)

  • Yanwei Zhang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Current research has ignored the hiddenness and the stochasticity of the evolution of public crisis management research, making the knowledge trajectories still unclear. This paper introduces a combined approach, LDA-HMM, to mine the hidden topics, present the evolutionary trajectories of the topics, and predict the future trends in the coming years to fill the research gaps. We reviewed 8543 articles in WOS from 1997 to 2021, extracted 39 hidden topics from the text using the LDA; 33 remained by manual labeling. The development of the topics over the years verifies that the topics are co-evolving with the public crisis events. The confusion and transition features indicate that most topics are confused or transferred to the others. The transition network and the direction of the topics show that six main transfer paths exist, and in the evolution process, the topics have become more focused. By training the HMM, we predict the trends in the next five years; the results show that the heat of the topic that focuses on traditional crisis issues will decrease while the focus on non-traditional issues will increase. We take the average error to test this model’s prediction effect by comparing it with the other approaches, concluding that it is better than the others. This study has practical implications for preventing crisis events, optimizing related policies, and grasping key research areas in the future.

Suggested Citation

  • Feng Wu & Wanqiang Xu & Chaoran Lin & Yanwei Zhang, 2022. "Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:1966-:d:833212
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    Cited by:

    1. Weibin Lin & Xianli Wu & Zhengwei Wang & Xiaoji Wan & Hailin Li, 2022. "Topic Network Analysis Based on Co-Occurrence Time Series Clustering," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
    2. Feng Wu & Yue Tang & Chaoran Lin & Yanwei Zhang & Wanqiang Xu, 2022. "Knowledge Trajectories Detection and Prediction of Modern Emergency Management in China Based on Topic Mining from Massive Literature Text," Sustainability, MDPI, vol. 14(24), pages 1-19, December.

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