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

Datos Bibliográficos

ID4652161
AutoresJianghao Wang (0000-0001-5333-3827, Chinese Academy of Sciences), Yichun Fan (0000-0001-8400-3863, Massachusetts Institute of Technology), Juan Palacios (0000-0003-4234-5114, Massachusetts Institute of Technology), Yuchen Chai (0000-0003-1921-7608, Massachusetts Institute of Technology), Nicolas Guetta-Jeanrenaud (0000-0002-7481-714X, Massachusetts Institute of Technology), Nick Obradovich (0000-0003-1127-2231, Max Planck Institute for Human Development), C Zhou (0000-0003-3331-2302, Chinese Academy of Sciences, autor de correspondencia), Siqi Zheng (0000-0002-4467-8505, Massachusetts Institute of Technology, autor de correspondencia)
Año2022
Volumen6
Número3
Páginas349-358
Fecha de publicación2022-03-17
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaNature Human Behaviour (JOURNAL)
Identificadores de la revistaISSN: 2397-3374 • E-ISSN: 2397-3374
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1038/s41562-022-01312-y
PMID35301467
OpenAlexW4220790135
IdiomaEN
Citas recibidas35
Referencias citadas58

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

2019-20 coronavirus outbreak · Biology · Outbreak · Pandemic · Emotions and Moral Behavior · Medicine · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Internal Medicine · Virology