COVID-19疫情防控中的中国公众舆情时空演变特征

doi:10.11821/dlxb202011016

地理学报 ›› 2020, Vol. 75 ›› Issue (11): 2490-2504.doi: 10.11821/dlxb202011016

• COVID-19疫情影响分析 • 上一篇下一篇

COVID-19疫情防控中的中国公众舆情时空演变特征

王卷乐¹^,⁵(), 张敏¹^,², 韩雪华¹^,², 王晓洁¹^,³, 郑莉¹^,⁴

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 山东理工大学建筑工程学院,淄博 255000
4. 防灾科技学院,三河 065201
5. 江苏省地理信息资源开发与利用协同创新中心,南京 210023

收稿日期:2020-05-08 修回日期:2020-10-12 出版日期:2020-11-25 发布日期:2021-01-25
作者简介:王卷乐(1976-), 男, 河南洛阳人, 博士, 研究员, 主要从事资源环境科学数据集成与共享研究。E-mail: wangjl@igsnrr.ac.cn
基金资助:
中国工程科技知识中心建设项目(CKCEST-2020-2-4);中—巴地球科学研究中心项目(Y99R0900F2);中国科学院信息化专项(XXH13505-07)

Spatio-temporal evolution and regional differences of the public opinion on the prevention and control of COVID-19 epidemic in China

WANG Juanle¹^,⁵(), ZHANG Min¹^,², HAN Xuehua¹^,², WANG Xiaojie¹^,³, ZHENG Li¹^,⁴

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, Shandong, China
4. Institute of Disaster Prevention, Sanhe 065201, Hebei, China
5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2020-05-08 Revised:2020-10-12 Published:2020-11-25 Online:2021-01-25
Supported by:
Construction Project of China Knowledge Centre for Engineering Sciences and Technology(CKCEST-2020-2-4);China-Pakistan Joint Research Centre on Earth Sciences(Y99R0900F2);The Informatization Plan of Chinese Academy of Sciences(XXH13505-07)

摘要/Abstract

摘要：

COVID-19疫情是全球面临的重大公共卫生危机。客观认识疫情期间的公众舆情响应和区域差异,对于提高重大公共卫生事件的政策调控和科学治理具有现实意义。本文以新浪微博为数据源,基于潜在狄利克雷分配主题模型和随机森林算法构建了主题抽取和分类模型,识别微博文本中的13个舆情话题,并从数量、空间、时间、内容等方面分析了2020年1月9日—3月10日在湖北省、京津冀、长三角、珠三角、成渝等城市群及沿边口岸等重点区域分布特点。结果表明：中国公众的响应总体是理性和积极的,但各舆情话题在区域内部的空间分布差异明显。各区域热点分布中,京津冀以首都北京为中心,长三角以上海为中心,辅以南京、杭州等热点,珠三角以广州、深圳为两核,湖北省以武汉为中心。建议应持续加强重点区域的疫情舆情关注和因地制宜的差异化精准响应。

关键词: COVID-19, 疫情防控, 公众舆情, 区域差异

Abstract:

The COVID-19 epidemic is a global public health crisis. It is of practical significance to objectively understand the public's responses and regional differences in order to improve policy control and scientific governance during major public health threats. In this study, a topic extraction and classification model was constructed based on the Latent Dirichlet Allocation topic model and the Random Forest algorithm. Thirteen topics were identified about public opinion in the Chinese SINA microblog from January 9 to March 10, 2020. The regional distribution characteristics were explored in terms of the amount, space, time sequence, and content in major urban agglomerations including Hubei Province, Beijing-Tianjin-Hebei urban agglomeration, Yangtze River Delta, Pearl River Delta, Chengdu-Chongqing region, and some border ports of China. The results showed that the spatio-temporal distribution of public opinion is related to the severity of the epidemic, degree of population aggregation, and level of economic development. The response of Chinese people is rational and positive, and the spatial distribution within these regions is obviously different. Among the regional hotspots, Beijing-Tianjin-Hebei region is centred on Beijing; the Yangtze River Delta is centred on Shanghai, followed by Nanjing, Hangzhou and other hotspots; the Pearl River Delta is centred on Guangzhou and Shenzhen; and Hubei Province is centred on Wuhan. The time series of topics in each region are synchronously related, but there are differences in timing sequence and periodic fluctuation in response time and intensity. The imbalance of resource allocation caused by the sharp rise of relief information in the short term is prominent, and the differences in response policies of various urban agglomerations combined with regional characteristics are not obvious. We should continue to focus on public opinion on epidemic situations in key areas and accurately respond to local regions according to its actual conditions.

Key words: COVID-19, epidemic prevention and control, public opinion, regional differences

王卷乐, 张敏, 韩雪华, 王晓洁, 郑莉. COVID-19疫情防控中的中国公众舆情时空演变特征[J]. 地理学报, 2020, 75(11): 2490-2504.

WANG Juanle, ZHANG Min, HAN Xuehua, WANG Xiaojie, ZHENG Li. Spatio-temporal evolution and regional differences of the public opinion on the prevention and control of COVID-19 epidemic in China[J]. Acta Geographica Sinica, 2020, 75(11): 2490-2504.

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区域	恐惧担忧	质疑当地政府/媒体	谴责恶习	客观评论	科学防疫	祈福祝愿	倡导救助	复工意愿	就医求助	物资求助	呼吁海外重视疫情	关心全球疫情	微博总数
京津冀	0.839^**	0.800^**	0.849^**	0.826^**	0.833^**	0.866^**	0.888^**	0.801^**	0.782^**	0.843^**	0.869^**	0.812^**	0.838^**
长三角	0.938^**	0.848^**	0.948^**	0.951^**	0.946^**	0.841^**	0.379	0.862^**	0.898^**	0.740^**	0.896^**	0.945^**	0.945^**
珠三角	0.872^**	0.852^**	0.879^**	0.881^**	0.875^**	0.919^**	0.312	0.830^**	0.562	0.314	0.829^**	0.853^**	0.884^**
成渝	0.889^**	0.607^**	0.878^**	0.849^**	0.848^**	0.833^**	0.605^**	0.882^**	0.542^**	0.941^**	0.847^**	0.710^**	0.853^**
湖北	0.819^**	0.797^**	0.796^**	0.797^**	0.832^**	0.808^**	0.814^**	0.819^**	0.743^**	0.775^**	0.787^**	0.811^**	0.807^**

COVID-19疫情防控中的中国公众舆情时空演变特征

Spatio-temporal evolution and regional differences of the public opinion on the prevention and control of COVID-19 epidemic in China

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