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

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.

Keywords

• public crisis management
• knowledge trajectories
• topic-clustered evolution extraction
• latent Dirichlet allocation
• hidden Markov model