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.

Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method

“…These measurements assist in the process of distinguishing topics semantically from topics that are artifacts of statistical inference. In addition, topic coherence is considered capable of providing a better interpretation of the topic modeling results compared to Perplexity [12]. The division of the number of topics in the LDA process is essential in the topic modelling evaluation process.…”

“…Therefore, LDA emerged as a solution to overcome this issue. Latent Dirichlet Allocation (LDA) is a generative model to find latent semantic topics in a text data set [12]. In addition, LDA is also a three-level hierarchical Bayesian model, where each item from a group of words is used to model a topic [13].…”

Measurement of Innovation in the Core Industry of China's Digital Economy: A Text-based Approach

“…So far, the existing literature mainly focuses on the number of patent applications in electronic information industry (Jia et al, 2021) and research and development input (Liu et doi: 10.36689/uhk/hed/2024(Liu et doi: 10.36689/uhk/hed/ -01-044 al., 2019Hao et al, 2020) conducted research, while ignoring the innovation of non-patented and non-R&D enterprises, which may make information omission. In order to further understand the innovation level of electronic information industry, this paper introduces Latent Dirichlet Allocation (LDA) topic model (Blei et al, 2003) to generate the text topic of analyst report, and uses its good characteristics to model massive heterogeneous text data (Wu et al, 2022). Fully associate the text word aggregation class, effectively measure the topic probability of new documents, and improve the overall accuracy and credibility of the measurement (Omar et al, 2015).…”