Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach
The effective control over the outbreak of COVID-19 in China showcases a prompt government response, in which, however, the allocation of attention, as an essential parameter, remains obscure. This study is designed to clarify the evolution of the Chinese government’s attention in tackling the pande...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
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| Series: | Healthcare |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9032/9/7/898 |
| _version_ | 1797527056178741248 |
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| author | Quan Cheng Jianhua Kang Minwang Lin |
| author_facet | Quan Cheng Jianhua Kang Minwang Lin |
| author_sort | Quan Cheng |
| collection | DOAJ |
| description | The effective control over the outbreak of COVID-19 in China showcases a prompt government response, in which, however, the allocation of attention, as an essential parameter, remains obscure. This study is designed to clarify the evolution of the Chinese government’s attention in tackling the pandemic. To this end, 674 policy documents issued by the State Council of China are collected to establish a text corpus, which is then used to extract policy topics by applying the latent dirichlet allocation (LDA) model, a topic modelling approach. It is found that the response policies take different tracks in a four-stage controlling process, and five policy topics are identified as major government attention areas in all stages. Moreover, a topic evolution path is highlighted to show internal relationships between different policy topics. These findings shed light on the Chinese government’s dynamic response to the pandemic and indicate the strength of applying adaptive governance strategies in coping with public health emergencies. |
| first_indexed | 2024-03-10T09:38:37Z |
| format | Article |
| id | doaj.art-ccd49d4ae3214bf0806b5664111d2a30 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T09:38:37Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-ccd49d4ae3214bf0806b5664111d2a302023-11-22T03:53:05ZengMDPI AGHealthcare2227-90322021-07-019789810.3390/healthcare9070898Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling ApproachQuan Cheng0Jianhua Kang1Minwang Lin2School of Economics and Management, Fuzhou University, Fuzhou 350108, ChinaSchool of Economics and Management, Fuzhou University, Fuzhou 350108, ChinaSchool of Economics and Management, Fuzhou University, Fuzhou 350108, ChinaThe effective control over the outbreak of COVID-19 in China showcases a prompt government response, in which, however, the allocation of attention, as an essential parameter, remains obscure. This study is designed to clarify the evolution of the Chinese government’s attention in tackling the pandemic. To this end, 674 policy documents issued by the State Council of China are collected to establish a text corpus, which is then used to extract policy topics by applying the latent dirichlet allocation (LDA) model, a topic modelling approach. It is found that the response policies take different tracks in a four-stage controlling process, and five policy topics are identified as major government attention areas in all stages. Moreover, a topic evolution path is highlighted to show internal relationships between different policy topics. These findings shed light on the Chinese government’s dynamic response to the pandemic and indicate the strength of applying adaptive governance strategies in coping with public health emergencies.https://www.mdpi.com/2227-9032/9/7/898COVID-19pandemicpolicy changegovernment attentiontext miningtopic evolution path |
| spellingShingle | Quan Cheng Jianhua Kang Minwang Lin Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach Healthcare COVID-19 pandemic policy change government attention text mining topic evolution path |
| title | Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach |
| title_full | Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach |
| title_fullStr | Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach |
| title_full_unstemmed | Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach |
| title_short | Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach |
| title_sort | understanding the evolution of government attention in response to covid 19 in china a topic modeling approach |
| topic | COVID-19 pandemic policy change government attention text mining topic evolution path |
| url | https://www.mdpi.com/2227-9032/9/7/898 |
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