Collective emotions during the COVID-19 outbreak

Area of Science:

• Psychology
• Sociology
• Computational Social Science

Background:

• The COVID-19 pandemic introduced global health and social disruptions.
• Uncertainty and government interventions likely influenced public emotional responses.

Purpose of the Study:

• To analyze digital traces of emotional expressions on Twitter during the early COVID-19 pandemic.
• To investigate how emotional language evolved across different countries and languages in response to the pandemic and public health measures.

Main Methods:

• Analysis of tweet data from 18 countries in six languages over five weeks post-outbreak.
• Quantification of emotional terms, particularly anxiety, sadness, and anger, in relation to case growth and government measure stringency.

Main Results:

• An initial surge in anxiety-related terms correlated with rising cases and stricter government measures.
• Anxiety expressions decreased with sustained measures, suggesting reassurance.
• Sadness increased, while anger decreased, with measure implementation, remaining stable thereafter.
• Positive emotion words showed only minor, brief decreases.
• A notable shift in tweet topics from political to pandemic-related issues was observed.

Conclusions:

• Early pandemic communication on social media reflected significant emotional shifts, with anxiety being prominent initially.
• Sustained public health measures appeared to stabilize emotional expressions, potentially indicating adaptation or reassurance.
• The analysis highlights the emergence of collective emotions and a significant change in public discourse towards pandemic concerns.
• Time-sensitive analysis of social media emotions offers valuable insights for public health risk communication strategies.