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Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis
Wang, Yilin1,2,3; Wu, Peijing1,2,4; Liu, Xiaoqian1,2; Li, Sijia1,2; Zhu, Tingshao1,2; Zhao, Nan1,2
第一作者Wang, Yilin
通讯作者邮箱zhaonan@psych.ac.cn
心理所单位排序1
摘要

Background: During the COVID-19 pandemic, residential lockdowns were implemented in numerous cities in China to contain the rapid spread of the disease. Although these stringent regulations effectively slowed the spread of COVID-19, they may have posed challenges to the well-being of residents. Objective: This study aims to explore the effects of residential lockdown on the subjective well-being (SWB) of individuals in China during the COVID-19 pandemic. Methods: The sample consisted of 1790 Sina Weibo users who were residents of cities that imposed residential lockdowns, of which 1310 users (73.18%) were female, and 3580 users who were residents of cities that were not locked down (gender-matched with the 1790 lockdown residents). In both the lockdown and nonlockdown groups, we calculated SWB indicators during the 2 weeks before and after the enforcement date of the residential lockdown using individuals' original posts on Sina Weibo. SWB was calculated via online ecological recognition, which is based on established machine learning predictive models. Results: The interactions of time (before the residential lockdown or after the residential lockdown) x area (lockdown or nonlockdown) in the integral analysis (N=5370) showed that after the residential lockdown, compared with the nonlockdown group, the lockdown group scored lower in some negative SWB indicators, including somatization (F-1,(5368) =13.593, P<. 001) and paranoid ideation (F-1,(5368) =14.333, P<.001). The interactions of time (before the residential lockdown or after the residential lockdown) x area (developed or underdeveloped) in the comparison of residential lockdown areas with different levels of economic development (N=1790) indicated that the SWB of residents in underdeveloped areas showed no significant change after the residential lockdown (P>.05), while that of residents in developed areas changed. Conclusions: These findings increase our understanding of the psychological impact and cost of residential lockdown during an epidemic. The more negative changes in the SWB of residents in developed areas imply a greater need for psychological intervention under residential lockdown in such areas.

关键词COVID-19 residential lockdown subjective well-being online ecological recognition
2020-12-17
DOI10.2196/24775
发表期刊JOURNAL OF MEDICAL INTERNET RESEARCH
ISSN1438-8871
卷号22期号:12页码:13
期刊论文类型实证研究
收录类别SCI
资助项目National Natural Science Foundation of China[31700984] ; Youth Innovation Promotion Association CAS ; National Key Research & Development Program of China[2016YFC1307200] ; China Social Science Fund[17AZD041]
出版者JMIR PUBLICATIONS, INC
WOS关键词HAPPINESS ; INTERNET
WOS研究方向Health Care Sciences & Services ; Medical Informatics
WOS类目Health Care Sciences & Services ; Medical Informatics
WOS记录号WOS:000602410700003
WOS分区Q1
引用统计
被引频次:22[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.psych.ac.cn/handle/311026/38304
专题中国科学院行为科学重点实验室
通讯作者Zhao, Nan
作者单位1.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, 16 Lincui Rd, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
3.Nankai Univ, Dept Psychol, Tianjin, Peoples R China
4.Beijing Univ Posts & Telecommun, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Wang, Yilin,Wu, Peijing,Liu, Xiaoqian,et al. Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2020,22(12):13.
APA Wang, Yilin,Wu, Peijing,Liu, Xiaoqian,Li, Sijia,Zhu, Tingshao,&Zhao, Nan.(2020).Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.JOURNAL OF MEDICAL INTERNET RESEARCH,22(12),13.
MLA Wang, Yilin,et al."Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis".JOURNAL OF MEDICAL INTERNET RESEARCH 22.12(2020):13.
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