Background News media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions. Objective Using data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking). Methods COVID-19–related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19–related official updates). Results In addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. Conclusions Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.

IntroductionRecent international and national strategies to reduce suicide mortality have suggested that social protection programmes may be an effective multisectoral response given the link between material deprivation and suicides in observational studies. However, there is a lack of evidence on the causal relationship between social protection programmes and suicide, which may hinder substantial national budget reallocations necessary to implement these policies. Social protection programmes are government interventions that ensure adequate income now and in the future, through changes to earned income (eg, minimum wage increase) or social security (via cash transfers or cash equivalents). Our review aims to evaluate the existing evidence on a causal relationship between social protection programmes and suicide mortality by examining all relevant experimental and quasi-experimental studies between January 1980 and November 2021.Methods and analysisThe review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. We will search references published between 1 January 1980 and 30 November 2021 in 10 electronic databases, including MEDLINE (PubMed), PsycINFO, EMBASE and Applied Social Sciences Index Abstracts. Seven reviewers will independently participate in screening studies from titles, abstracts and full texts across all the stages. Experimental (ie, randomised controlled trials) and quasi-experimental studies (ie, non-randomised interventional studies) written in English, French, Spanish, German, Chinese, Korean and Japanese examining the impact of income security programmes on suicide mortality were included. Meta-analyses will be conducted if there are at least three studies with similar income security programmes.Ethics and disseminationOur proposed review does not require ethical approval. In collaboration with our community partners, we will develop a policy brief for stakeholders to support efforts to implement social protection programmes to help prevent suicides. Our findings will be presented at conferences, published in a peer-reviewer journal and promoted on social media platforms.PROSPERO registration numberCRD42021252235.

BACKGROUND News media coverage of anti-mask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views, but does little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policymakers to craft better public health messages in anticipation of poor reactions to controversial restrictions. OBJECTIVE Using data from social media, this study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (e.g. business and school closure, regional lockdown differences, additional public health restrictions such as social distancing and masking). METHODS COVID-related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 to Oct 31 2020. Sentiment scores were calculated using the VADER algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites, and dynamic regression models with ARIMA errors were used to examine the association between public health restrictions and changes in public opinion over time (i.e. collective attention, aggregate positive sentiment, and level of disagreement) controlling for the effects of confounders (i.e. daily COVID-19 case counts, holidays, COVID-related official updates). RESULTS In addition to expected direct effects (e.g. business closure led to decreased positive sentiment and increased disagreements), the impact of restriction on public opinion is contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closure and other restrictions (e.g. masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (i.e. sentiment polarization). Partial (region-targeted) lockdowns were associated with better public response (i.e. higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. CONCLUSIONS Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policymakers anticipate public response to future pandemic restrictions, and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.

Background: News media coverage of anti-mask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views, but does little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policymakers to craft better public health messages in anticipation of poor reactions to controversial restrictions. Objective: Using data from social media, this study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (e.g. business and school closure, regional lockdown differences, additional public health restrictions such as social distancing and masking). Methods: COVID-related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 to Oct 31 2020. Sentiment scores were calculated using the VADER algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites, and dynamic regression models with ARIMA errors were used to examine the association between public health restrictions and changes in public opinion over time (i.e. collective attention, aggregate positive sentiment, and level of disagreement) controlling for the effects of confounders (i.e. daily COVID-19 case counts, holidays, COVID-related official updates). Results: In addition to expected direct effects (e.g. business closure led to decreased positive sentiment and increased disagreements), the impact of restriction on public opinion is contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closure and other restrictions (e.g. masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (i.e. sentiment polarization). Partial (region-targeted) lockdowns were associated with better public response (i.e. higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. Conclusions: Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policymakers anticipate public response to future pandemic restrictions, and ensure adequate