The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.
Social 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.
Public behavior in cyberspace is extremely sensitive to emergency disaster events. Using appropriate methodologies to capture the semantic evolution of social media users’ behaviors and discover how it varies across geographic space and time still presents a significant challenge. This study proposes a novel framework based on complex network, topic model, and GIS to describe the topic change of social media users’ behaviors during disaster events. The framework employs topic modeling to extract topics from social media texts, builds a user semantic evolution model based on a complex network to describe topic dynamics, and analyzes the spatio-temporal characteristics of public semantics evolution. The proposed framework has demonstrated its effectiveness in analyzing the semantic spatial–temporal evolution of Chinese Weibo user behavior during COVID-19. The semantic change in response to COVID-19 was characterized by obvious expansion, frequent change, and gradual stabilization over time. In this case, there were obvious geographical differences in users’ semantic changes, which were mainly concentrated in the capital and economically developed areas. The semantics of users finally focused on specific topics related to positivity, epidemic prevention, and factual comments. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions. In emergency situations, this model could improve situational assessment, assist decision makers to better comprehend public opinion, and support analysts in allocating resources of disaster relief appropriately.
Simulating the dynamic process of urban resilience and analyzing the mechanism of resilience-influencing factors are of great significance to improve the intelligent decision-making ability of resilient urban planning. The purpose of this article is to implement a comprehensive literature review on the quantitative computation and simulation studies of urban resilience, investigating the characteristics of current research, including the most commonly applied methods, the most frequently space–time scales, the most popular research topics, and the most commonly involved risk types. Then, the study provides recommendations for future research: (1) research on multiple risk disturbance scenarios, (2) the computation of urban resilience from the public perspective, and (3) a computation-simulation framework with the goal of revealing the mechanism. Finally, this study constructs a resilience-computation simulation framework for resilient urban planning, which lays a foundation for the further development of urban-resilience dynamic-simulation computing and planning-scenario applications in the future.
Web text, using natural language to describe a disaster event, contains a considerable amount of disaster information. Automatic extraction from web text of this disaster information (e.g., time, location, casualties, and disaster losses) is an important supplement to conventional disaster monitoring data. This study extracted and compared the characteristics of earthquake disaster information from web news media reports (news reports) and online disaster reduction agency reports (professional reports). Using earthquakes in China from 2015 to 2017 as a case study, a series of rules were created for extracting earthquake event information, including temporal extraction rules, a location trigger dictionary, and an attribute trigger dictionary. The differences in characteristics of news reports and professional reports were investigated in terms of their quantity and spatiotemporal distribution through statistical analysis, geocoding, and kernel density estimation. The information extracted from each set of reports was also compared with authoritative data. The results indicated that news reports are more extensive and have richer information. In contrast, professional reports are less repetitive as well as more accurate and standardized, mainly focusing on earthquakes with Ms ≥ 4 and/or earthquakes that may cause damage. These characteristics of disaster information from different web texts sources can be used to improve the efficiency and analysis of disaster information extraction. In addition, the rule-based approach proposed herein was found to be an accurate and viable way to extract earthquake information from web texts. The approach provided the technical basics and background information to support further research seeking human-centric disaster information, which cannot be acquired using traditional instrument monitoring methods, from web text.
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