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Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China
Han, Xuehua1,2,3; Wang, Juanle1,3,4
2019-04-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume8Issue:4Pages:16
Corresponding AuthorWang, Juanle(wangjl@igsnrr.ac.cn)
AbstractSocial media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to public sentiment, among which questioning the government and media was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management.
Keywordsocial media flood public sentiment disaster risk reduction China
DOI10.3390/ijgi8040185
WOS KeywordTWITTER
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program (class A) of the Chinese Academy of Sciences[XDA19040501] ; National Natural Science Foundation of China[41421001] ; Construction Project of China Knowledge Centre for Engineering Sciences and Technology[CKCEST-2018-2-8]
Funding OrganizationStrategic Priority Research Program (class A) of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Construction Project of China Knowledge Centre for Engineering Sciences and Technology
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000467499300026
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/59677
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Juanle
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.IKCEST, Beijing 100088, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Han, Xuehua,Wang, Juanle. Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(4):16.
APA Han, Xuehua,&Wang, Juanle.(2019).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,8(4),16.
MLA Han, Xuehua,et al."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 8.4(2019):16.
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