Determining containment policy impacts on public sentiment during the pandemic using social media data

Prakash Chandra Sukhwal and Atreyi Kankanhalli
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Prakash Chandra Sukhwal: a Institute of Systems Science, National University of Singapore, 119615 Singapore;
Atreyi Kankanhalli: b Department of Information Systems and Analytics, National University of Singapore, 117417 Singapore

Proceedings of the National Academy of Sciences, 2022, vol. 119, issue 19, e2117292119

Abstract: For effective pandemic response, policymakers need tools that can assess policy impacts in near real-time. This requires policymakers to monitor changes in public well-being due to policy interventions. Particularly, containment measures affect people’s mental well-being, yet changes in public emotions and sentiments are challenging to assess. Our work provides a solution by using social media posts to compute salient concerns and daily public sentiment values as a proxy of mental well-being. We demonstrate how public sentiment and concerns are impacted by various containment policy sub-types. This approach provides key benefits of using a data-driven approach to identify public concerns and provides near real-time assessment of policy impacts by computing daily public sentiment based on postings on social media.

Keywords: COVID-19; containment policies; public sentiment; social media data; causal analysis (search for similar items in EconPapers)
Date: 2022
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