Literature DB >> 33290247

Subjective Well-Being of Chinese Sina Weibo Users in Residential Lockdown During the COVID-19 Pandemic: Machine Learning Analysis.

Yilin Wang1,2,3, Peijing Wu1,2,4, Xiaoqian Liu1,2, Sijia Li1,2, Tingshao Zhu1,2, Nan Zhao1,2.   

Abstract

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) × 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 (F1,5368=13.593, P<.001) and paranoid ideation (F1,5368=14.333, P<.001). The interactions of time (before the residential lockdown or after the residential lockdown) × 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. ©Yilin Wang, Peijing Wu, Xiaoqian Liu, Sijia Li, Tingshao Zhu, Nan Zhao. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.12.2020.

Entities:  

Keywords:  COVID-19; online ecological recognition; residential lockdown; subjective well-being

Year:  2020        PMID: 33290247     DOI: 10.2196/24775

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  5 in total

1.  Developmental Trend of Subjective Well-Being of Weibo Users During COVID-19: Online Text Analysis Based on Machine Learning Method.

Authors:  Yingying Han; Wenhao Pan; Jinjin Li; Ting Zhang; Qiang Zhang; Emily Zhang
Journal:  Front Psychol       Date:  2022-01-06

2.  Understanding the conversation around COVID-19 and eating disorders: A thematic analysis of Reddit.

Authors:  Ashleigh N Shields; Elise Taylor; Jessica R Welch
Journal:  J Eat Disord       Date:  2022-01-15

Review 3.  Challenges in translational machine learning.

Authors:  Artuur Couckuyt; Ruth Seurinck; Annelies Emmaneel; Katrien Quintelier; David Novak; Sofie Van Gassen; Yvan Saeys
Journal:  Hum Genet       Date:  2022-03-04       Impact factor: 5.881

Review 4.  Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review.

Authors:  Samantha J Teague; Adrian B R Shatte; Emmelyn Weller; Matthew Fuller-Tyszkiewicz; Delyse M Hutchinson
Journal:  JMIR Ment Health       Date:  2022-02-28

5.  The Impact of Mortality Salience, Negative Emotions and Cultural Values on Suicidal Ideation in COVID-19: A Conditional Process Model.

Authors:  Feng Huang; Sijia Li; Dongqi Li; Meizi Yang; Huimin Ding; Yazheng Di; Tingshao Zhu
Journal:  Int J Environ Res Public Health       Date:  2022-07-27       Impact factor: 4.614

  5 in total

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