Impact of COVID-19 Policies and Misinformation on Social Unrest
This addresses the problem of forecasting social instability during crises for policymakers, but it is incremental with mixed results.
The study tackled predicting social unrest (protests) during the COVID-19 pandemic by analyzing health outcomes, policies, and misinformation in Western Europe and the U.S., finding that at least one feature was predictive in most regions, but forecasts outperformed a naive model in only about half of the countries.
The novel coronavirus disease (COVID-19) pandemic has impacted every corner of earth, disrupting governments and leading to socioeconomic instability. This crisis has prompted questions surrounding how different sectors of society interact and influence each other during times of change and stress. Given the unprecedented economic and societal impacts of this pandemic, many new data sources have become available, allowing us to quantitatively explore these associations. Understanding these relationships can help us better prepare for future disasters and mitigate the impacts. Here, we focus on the interplay between social unrest (protests), health outcomes, public health orders, and misinformation in eight countries of Western Europe and four regions of the United States. We created 1-3 week forecasts of both a binary protest metric for identifying times of high protest activity and the overall protest counts over time. We found that for all regions, except Belgium, at least one feature from our various data streams was predictive of protests. However, the accuracy of the protest forecasts varied by country, that is, for roughly half of the countries analyzed, our forecasts outperform a naïve model. These mixed results demonstrate the potential of diverse data streams to predict a topic as volatile as protests as well as the difficulties of predicting a situation that is as rapidly evolving as a pandemic.