COVID-19疫情防控中的中国公众舆情时空演变特征

doi:10.11821/dlxb202011016

Abstract

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

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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[1]	World Health Organization. WHO statement on cases of COVID-19 surpassing 100000. https://www.who.int/news/item/07-03-2020-who-statement-on-cases-of-covid-19-surpassing-100-000.
[2]	World Health Organization. WHO statement on cases of COVID-19 could be characterized as a pandemic, Available online: https://www.who.int/news-room/detail/29-06-2020-covidtimeline.
[3]	The State Council Information Office of the People's Republic of China. Fighting Covid-19 China in Action. (2020-06-07) [2020-06-12]. http://www.scio.bov.cn/zfbps/32832/Document/1681801.htm.
	中华人民共和国国务院新闻办公室. 《抗击新冠肺炎疫情的中国行动》白皮书. (2020-06-07) [2020-06-12]. http://www.scio.bov.cn/zfbps/32832/Document/1681801.htm.]
[4]	Chen E, Lerman K, Ferrara E. COVID-19: The first public coronavirus twitter dataset. 2020. ArXiv: 2003.07372.
[5]	Banda J M, Tekumalla R, Wang G, et al. A large-scale COVID-19 Twitter chatter dataset for open scientific research: An international collaboration. 2020. ArXiv:2004.03688.
[6]	Yin F L, Lv J H, Zhang X Y, et al. COVID-19 information propagation dynamics in the Chinese Sina-microblog. Mathematical Biosciences and Engineering, 2020,17(3):2676-2692. doi: 10.3934/mbe.2020146 pmid: 32233560
[7]	Gao Wenbin, Chen Zhiyan, Wang Yiniu, et al. The dynamic analysis of public concern during SARS epidemic period. Chinese Mental Health Journal, 2003,17(9):594-596.
	[ 高文斌, 陈祉妍, 王一牛, 等. SARS疫情期间公众心态影响及变化趋势分析. 中国心理卫生杂志, 2003,17(9):594-596.]
[8]	Lwin M O, Lu J, Sheldenkar A, et al. Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends. JMIR public health and surveillance, 2020,6(2):e19447. Doi: 10.2196/19447. doi: 10.2196/19447 pmid: 32412418
[9]	Han X H, Wang J L, Zhang M, et al. Using social media to mine and analyze public opinion related to COVID-19 in China. International Journal of Environmental Research and Public Health, 2020,17(8):2788. Doi: 10.3390/ ijerph17082788. doi: 10.3390/ijerph17082788
[10]	Xing Pengfei, Li Xinxin. Research on formation mechanism of online public opinions and guiding strategy in major epidemic prevention and control: Qualitative analysis based on online public opinion texts during the outbreak of COVID-19. Journal of Intelligence, 2020,39(7):67-74, 158.
	[ 邢鹏飞, 李鑫鑫. 重大疫情防控中网络舆情形成机制及引导策略研究: 基于新冠肺炎疫情期间网络舆情文本的质性分析. 情报杂志, 2020,39(7):67-74, 158.]
[11]	Chen Xingshu, Chang Tianyou, Wang Haizhou, et al. Spatial and temporal analysis on public opinion evolution of epidemic situation about novel coronavirus pneumonia based on micro-blog data. Journal of Sichuan University (Natural Science Edition), 2020,57(2):409-416.
	[ 陈兴蜀, 常天祐, 王海舟, 等. 基于微博数据的“新冠肺炎疫情”舆情演化时空分析. 四川大学学报(自然科学版), 2020,57(2):409-416.]
[12]	Li L F, Zhang Q P, Wang X, et al. Characterizing the propagation of situational information in social media during COVID-19 epidemic: A case study on Weibo. IEEE Transactions on Computational Social Systems, 2020,7(2):556-562. doi: 10.1109/TCSS.6570650
[13]	Dian A, Noviyanti P. Social media analysis: Utilization of social media data for research on COVID-19. https://www.researchgate.net/publication/340511574https://www.researchgate.net/publication/340511574.
[14]	Rosenberg H, Syed S, Rezaie S. The Twitter pandemic: The critical role of twitter in the dissemination of medical information and misinformation during the COVID-19 Pandemic. Canadian Journal of Emergency Medicine, 2020,22(4):1-7.
[15]	Nelson L M, Simard J F, Oluyomi A, et al. US public concerns about the COVID-19 pandemic from results of a survey given via social media. JAMA Internal Medicine, 2020,180(7). Doi: 10.1001/jamainternmed.2020.1369. doi: 10.1001/jamainternmed.2020.1602 pmid: 32421168
[16]	Sina Finance. Weibo releases financial report for the fourth quarter and the whole year of 2019. 2020-02-26. http://finance.sina.com.cn/stock/usstock/c/2020-02-26/doc-iimxyqvz6006957.shtml.
	新浪财经. 微博发布2019年第四季度及全年财报. 2020-02-26. http://finance.sina.com.cn/stock/usstock/c/2020-02-26/doc-iimxyqvz6006957.shtml.]
[17]	Dahal B Kumar S A P Li Z. Topic modeling and sentiment analysis of global climate change tweets. Social Network Analysis and Mining, 2019,9:24. Doi: 10.1007/s13278-019-0568-8. doi: 10.1007/s13278-019-0568-8
[18]	Ye X, Li S, Yang X, et al. Use of social media for the detection and analysis of infectious diseases in China. ISPRS International Journal of Geo-Information. 2016,5:156. Doi: 10.3390/ijgi5090156. doi: 10.3390/ijgi5090156
[19]	Han X H, Wang J L. Using social media to mine and analyze public sentiment during a disaster: A case study of the 2018 Shouguang City flood in China. ISPRS International Journal of Geo-Information, 2019,8(4):185. Doi: 10.3390/ijgi8040185. doi: 10.3390/ijgi8040185
[20]	Saffari A, Leistner C, Santner J, et al. On-line random forests//Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, Japan. 27 September-4 October, 2009. Doi: 10.1109/ICCVW.2009.5457447.
[21]	Yuan Y H, Lu Y M, Chow T E, et al. The missing parts from social media-enabled smart cities: Who, where, when, and what? Annals of the American Association of Geographers, 2020,110(2):462-475. doi: 10.1080/24694452.2019.1631144

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^**

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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