Global evidence of expressed sentiment alterations during the COVID-19 pandemic

Jianghao Wang, Yichun Fan, Juan Palacios, Yuchen Chai, Nicolas Guetta-Jeanrenaud, Nick Obradovich, Chenghu Zhou () and Siqi Zheng ()
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Jianghao Wang: Chinese Academy of Sciences
Yichun Fan: Massachusetts Institute of Technology
Juan Palacios: Massachusetts Institute of Technology
Yuchen Chai: Massachusetts Institute of Technology
Nicolas Guetta-Jeanrenaud: Massachusetts Institute of Technology
Nick Obradovich: Max Planck Institute for Human Development
Chenghu Zhou: Chinese Academy of Sciences
Siqi Zheng: Massachusetts Institute of Technology

Nature Human Behaviour, 2022, vol. 6, issue 3, 349-358

Abstract: Abstract The COVID-19 pandemic has created unprecedented burdens on people’s physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.

Date: 2022
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DOI: 10.1038/s41562-022-01312-y

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