论文标题
大流行脉搏:在19日大流行期间拆开和建模社会信号
Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic
论文作者
论文摘要
我们介绍并开始探索一系列社会数据,这些数据代表了Covid-19的一部分大流行对美国的影响。这些数据是从一系列来源收集的,包括新闻主题,社会疏远行为,社区流动性变化,网络搜索等等的纵向趋势。这种多模式的努力为分析大流行对社会脉搏的影响提供了新的机会。我们的初步结果表明,在世界卫生组织在3月11日宣布大流行之后,与COVID-19与19的新闻报道的数量不断稳步下降 - 无论案件或公共政策数量的变化如何变化。此外,我们发现,相对于大流行开始之前测量的基线,政治上适中的和科学的来源具有比更政治上极端的来源发表的COVID-19新闻的比例较低。我们建议对这些多模式信号的进一步分析可以产生有意义的社会见解,并提出互动仪表板,以帮助进一步探索。
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.