2021/10/07 by Martha Barnard, Barnard, Martha, Radhika Iyer +5
Computer Science · Mathematics · Medicine · Social Sciences · #2019-20 coronavirus outbreak #Applications (stat.AP) #COVID-19 epidemiological studies #Computers and Society (cs.CY) #Coronavirus disease 2019 (COVID-19) #FOS: Computer and information sciences #Law #Machine Learning (cs.LG) #Media Influence and Politics #Medicine #Misinformation #Misinformation and Its Impacts #Outbreak #Political science #Politics #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #Social media #Social unrest #Unrest #Virology #cs.CY #cs.LG #stat.AP
paper · pdf · doi:10.48550/arxiv.2110.09234
published in arXiv (Cornell University) (Cornell University) · 21 pages, 9 figures
arxiv created 2021/10/07 · openalex publication_date 2021/10/07 · arxiv updated 2021/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.