A repeated-measures study on emotional responses after a year in the pandemic

Author:

Mozes Maximilian,van der Vegt Isabelle,Kleinberg Bennett

Abstract

AbstractThe introduction of COVID-19 lockdown measures and an outlook on return to normality are demanding societal changes. Among the most pressing questions is how individuals adjust to the pandemic. This paper examines the emotional responses to the pandemic in a repeated-measures design. Data (n = 1698) were collected in April 2020 (during strict lockdown measures) and in April 2021 (when vaccination programmes gained traction). We asked participants to report their emotions and express these in text data. Statistical tests revealed an average trend towards better adjustment to the pandemic. However, clustering analyses suggested a more complex heterogeneous pattern with a well-coping and a resigning subgroup of participants. Linguistic computational analyses uncovered that topics and n-gram frequencies shifted towards attention to the vaccination programme and away from general worrying. Implications for public mental health efforts in identifying people at heightened risk are discussed. The dataset is made publicly available.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference40 articles.

1. Institute for Government Analysis. Timeline of UK coronavirus lockdowns, March 2020 to March 2021. https://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf (2021).

2. Biester, L., Matton, K., Rajendran, J., Provost, E. M. & Mihalcea, R. Quantifying the effects of COVID-19 on mental health support forums. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020 (2020).

3. Boon-Itt, S. & Skunkan, Y. Public perception of the COVID-19 pandemic on Twitter: Sentiment analysis and topic modeling study. JMIR Public Health Surveill. 6, e21978. https://doi.org/10.2196/21978 (2020).

4. Shuja, J., Alanazi, E., Alasmary, W. & Alashaikh, A. COVID-19 open source data sets: a comprehensive survey. Appl. Intell. 51, 1–30 (2020).

5. Kleinberg, B., van der Vegt, I. & Mozes, M. Measuring emotions in the COVID-19 real world worry dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020. Online: Association for Computational Linguistics. https://www.aclweb.org/anthology/2020.nlpcovid19-acl.11 (2020).

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3