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 na\"ive 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.
翻译:新的冠状病毒(COVID-19)传染病(COVID-19)对地球每个角落都产生了影响,扰乱了政府,并导致社会经济不稳定。这一危机引发了围绕社会不同部门在变化和压力时期如何互动和相互影响的疑问。鉴于这一流行病的空前经济和社会影响,许多新的数据源已经出现,使我们能够从数量上探索这些联系。了解这些关系可以帮助我们更好地为今后的灾害做准备并减轻影响。在这里,我们侧重于西欧八个国家和美国四个区域的社会动乱(测试)、健康结果、公共卫生命令和错误信息之间的相互作用。我们制作了1-3周的二进制抗议指标,用于确定抗议活动高时段和总体抗议计时。我们发现,除了比利时之外,所有地区至少有一个不同数据流的特征是抗议的预测。然而,各国抗议预测的准确性是,对大约一半分析的国家来说,我们的预测结果超越了一种“感知”模型。这些混合的结果表明,不同的数据流有可能预测一个动荡的话题,作为抗议的一种快速变化的预测,作为一种大流行的预测,是一种快速的难题是迅速的预测。