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

Abstract

Stringent containment and closure policies have been widely implemented by governments to prevent the transmission of COVID-19. Yet, such policies have significant impacts on people's emotions and mental well-being. Here, we study the effects of pandemic containment policies on public sentiment in Singapore. We computed daily sentiment values scaled from 21 to 1, using high-frequency data of ∼240,000 posts from highly followed public Facebook groups during January to November 2020. The lockdown in April saw a 0.1 unit rise in daily average sentiment, followed by a 0.2 unit increase with partially lifting of lockdown in June, and a 0.15 unit fall after further easing of restrictions in August. Regarding the impacts of specific containment measures, a 0.13 unit fall in sentiment was associated with travel restrictions, whereas a 0.18 unit rise was related to introducing a facial covering policy at the start of the pandemic. A 0.15 unit fall in sentiment was linked to restrictions on public events, post lock-down. Virus infection, wearing masks, salary, and jobs were the chief concerns found in the posts. A 2 unit increase in these concerns occurred even when some restrictions were eased in August 2020. During pandemics, monitoring public sentiment and concerns through social media supports policymakers in multiple ways. First, the method given here is a near real-time scalable solution to study policy impacts. Second, it aids in data-driven and evidence-based revision of existing policies and implementation of similar policies in the future. Third, it identifies public concerns following policy changes, addressing which can increase trust in governments and improve public sentiment.