TY - JOUR
T1 - Global evidence of expressed sentiment alterations during the COVID-19 pandemic
AU - Wang, Jianghao
AU - Fan, Yichun
AU - Palacios Temprano, Juan Francisco
AU - Chai, Yuchen
AU - Guetta-Jeanrenaud, Nicolas
AU - Obradovich, Nick
AU - Zhou, Chenghu
AU - Zheng, Siqi
N1 - data source: The data used in this paper are available at https://github.com/Jianghao/Sentiment_COVID-19
PY - 2022/3/17
Y1 - 2022/3/17
N2 - 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.
AB - 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.
U2 - 10.1038/s41562-022-01312-y
DO - 10.1038/s41562-022-01312-y
M3 - Article
C2 - 35301467
SN - 2397-3374
VL - 6
SP - 349
EP - 358
JO - Nature human behaviour
JF - Nature human behaviour
IS - 3
ER -