[Data & Code ]Global evidence of expressed sentiment alterations during the COVID-19 pandemic

[chengjun]

https://github.com/SocratesAcademy/Sentiment_COVID-19

nature nature human behaviour articles article
Article
Published: 17 March 2022
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 & Siqi Zheng
Nature Human Behaviour volume 6, pages349–358 (2022)Cite this article

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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.

a, Standardized sentiment index by country (labelled with ISO three-letter country codes). The vertical line shows the time the WHO declared COVID-19 a global pandemic, and the purple line represents the sentiment nadir (that is, the lowest point) of each country. The countries are labelled with ISO 3166-1 alpha-3 country codes. b, Mapping of sentiment drop variations across countries. Sentiment drop is defined by the magnitude of sentiment decline from each country’s average sentiment before COVID-19 to its lowest sentiment value and measured by RDD (Methods, ‘Sentiment alterations during the COVID-19 pandemic’). The magnitude of drops is measured in standard deviations before COVID-19. Red represents large shock; green represents small shock. The box plot shows the median (interquartile range) of sentiment shock sizes in s.d. c, Mapping of recovery half-life variations across countries. Light colours correspond to quick recovery, blue indicates long recovery and purple indicates that the country is still in the recovering stage by 25 May 2020 (that is, recovery degree is below −1 s.d.). The circle sizes further display the recovery degrees by country. The box plot shows the median (interquartile range) of recovery half-life in days.

a, Average changes in sentiment scores associated with lockdown policies. b, Distribution of changes in sentiment associated with the enforcement of a national lockdown for the 52 countries in our sample that enforced a lockdown during our sample period (see Methods, ‘Impacts of lockdowns on expressed sentiment’, for the definition). Supplementary analyses at the subnational level for the United States are included in Supplementary Note 6. The effect is measured by comparing the standardized sentiment index with the pre-COVID-19 sentiment average for each country. The countries are labelled with ISO 3166-1 alpha-3 country codes. The flag SVG assets, used under the CC-BY 4.0 license, are taken from the Emojitwo set: https://emojitwo.github.io/.

Data availability
The data used in this paper are available at https://github.com/Jianghao/Sentiment_COVID-19.

Code availability
The code used in this paper is available at https://github.com/Jianghao/Sentiment_COVID-19.

[chengjun]

合成控制法和断点回归部分也可以将来用作因果推断的复刻案例，给硕士阶段的同学用

[chengjun]

Athey, S., & Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3-32.