Impact of COVID-19 Policies and Misinformation on Social Unrest

• URL: http://arxiv.org/abs/2110.09234v1
• Date: Thu, 7 Oct 2021 16:05:10 GMT
• Title: Impact of COVID-19 Policies and Misinformation on Social Unrest
• Authors: Martha Barnard (1), Radhika Iyer (1 and 2), Sara Y. Del Valle (1), Ashlynn R. Daughton (1) ((1) A-1 Information Systems and Modeling, Los Alamos National Lab, Los Alamos, NM, USA, (2) Department of Political Science and Department of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA, USA)
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Event Detection from Social Media for Epidemic Prediction [76.90779562626541]
  We develop a framework to extract and analyze epidemic-related events from social media posts. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics. We show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
  arXiv  Detail & Related papers  (2024-04-02T06:31:17Z)
• #StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs [23.67939019353524]
  COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. Existing studies rely on the aggregation of traditional statistical models and epidemic spread theory. We propose a novel framework, Social Media enhAnced pandemic knowledge based on the extracted events and relationships.
  arXiv  Detail & Related papers  (2021-08-08T15:46:05Z)
• When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
  We present CovidEmo, 1K tweets labeled with emotions. We examine how well large pre-trained language models generalize across domains and crises.
  arXiv  Detail & Related papers  (2021-07-23T04:07:14Z)
• Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study [0.0]
  This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy. Overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019.
  arXiv  Detail & Related papers  (2021-01-19T15:51:53Z)
• Temporal Clustering of Disorder Events During the COVID-19 Pandemic [0.0]
  We study the temporal nature of pandemic-related disorder events as tallied by the "COVID-19 Disorder Tracker" initiative. We find that disorder events are inter-dependent and self-excite in all three countries. Considerable diversity is observed among countries when computing correlations of events between subnational clusters.
  arXiv  Detail & Related papers  (2021-01-16T15:34:42Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• The Past, Present, and Future of COVID-19: A Data-Driven Perspective [4.373183416616983]
  We report results on our development and deployment of a web-based integrated real-time operational dashboard as an important decision support system for COVID-19. We conducted data-driven analysis based on available data from diverse authenticated sources to predict upcoming consequences of the pandemic. We also explored correlations between pandemic spread and important socio-economic and environmental factors.
  arXiv  Detail & Related papers  (2020-08-12T19:03:57Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic [12.050597862123313]
  We present and begin to explore a collection of social data that represents part of the COVID-19 pandemic's effects on the United States. This data is collected from a range of sources and includes longitudinal trends of news topics, social distancing behaviors, community mobility changes, web searches, and more.
  arXiv  Detail & Related papers  (2020-06-10T17:55:44Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic [66.80677233314002]
  Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
  arXiv  Detail & Related papers  (2020-04-21T13:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.