A global portrait of expressed mental health signals towards COVID-19 in social media space

Siqin Wang*, Xiao Huang, Tao Hu, Bing She, Mengxi Zhang, Ruomei Wang, Oliver Gruebner, Muhammad Imran, Jonathan Corcoran, Yan Liu, Shuming Bao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.

Original languageEnglish
Article number103160
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume116
DOIs
Publication statusPublished - Feb 2023

Keywords

  • COVID-19
  • Multilingual tweets
  • Negative sentiments
  • Pandemic
  • Policy implementation
  • Sentiment analysis
  • Social media

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