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

Jianghao Wang, Yichun Fan, Juan Francisco Palacios Temprano, Yuchen Chai, Nicolas Guetta-Jeanrenaud, Nick Obradovich, Chenghu Zhou*, Siqi Zheng*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to) 349–358
Number of pages12
JournalNature human behaviour
Volume6
Issue number3
DOIs
Publication statusPublished - 17 Mar 2022
Externally publishedYes

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