Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data

被引:20
|
作者
Shen, Lining [1 ,2 ,3 ]
Yao, Rui [1 ]
Zhang, Wenli [1 ]
Evans, Richard [4 ]
Cao, Guang [1 ]
Zhang, Zhiguo [1 ,2 ]
机构
[1] Huazhong Univ Sci & Technol, Tongji Med Coll, Sch Med & Hlth Management, 13 Hangkong Rd, Wuhan 430030, Peoples R China
[2] Hubei Prov Res Ctr Hlth Technol Assessment, Wuhan, Peoples R China
[3] Huazhong Univ Sci & Technol, Inst Smart Hlth, Wuhan, Peoples R China
[4] Brunel Univ London, Coll Engn Design & Phys Sci, London, England
关键词
COVID-19; Sina Weibo; social distancing measures; emotional analysis; machine learning; moderating effects; deep learning; social media; emotion; attitude; infodemiology; infoveillance; RISK INFORMATION-SEEKING; SENTIMENT; INFLUENZA; EPIDEMIC; MODEL;
D O I
10.2196/27079
中图分类号
R-058 [];
学科分类号
摘要
Background: Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions. Objective: The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People's Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users' emotions, was studied. Methods: Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users' emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions were assessed by observing the interaction effects between the measures and explanatory variables. Results: Based on the 63,169 comments obtained, we identified six topics of discussion-(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (chi(2)(8)=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (chi(2)(20)=19,084.73, P<.001), as well as emotional polarity between genders (chi(2)(4)=1784.59, P<.001) and emotional polarity between spatial locations (chi(2)(4)=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion. Conclusions: Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.
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页数:18
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