Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2006.05983v1
• Date: Wed, 10 Jun 2020 17:55:44 GMT
• Title: Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic
• Authors: Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla
• Abstract summary: 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.
• Score: 12.050597862123313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. This multimodal effort enables new opportunities for analyzing the impacts such a pandemic has on the pulse of society. Our preliminary results show that the number of COVID-19-related news articles published immediately after the World Health Organization declared the pandemic on March 11, and that since that time have steadily decreased---regardless of changes in the number of cases or public policies. Additionally, we found that politically moderate and scientifically-grounded sources have, relative to baselines measured before the beginning of the pandemic, published a lower proportion of COVID-19 news than more politically extreme sources. We suggest that further analysis of these multimodal signals could produce meaningful social insights and present an interactive dashboard to aid further exploration.

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