RAPID: How Scientific Data, Knowledge, and Expertise Mobilize in Online Media during the COVID-19 Crisis

在新冠肺炎大流行的同时，全球各地的人们都在经历一个信息生态系统，这个生态系统充斥着信息--有些是准确的，有些不是那么准确。危机事件本质上是不确定和焦虑的时期，在这种情况下，人们试图理解信息泛滥的意义。这一过程被称为集体制造感觉，具有心理和信息益处，但它也使我们容易受到错误信息传播的影响。围绕新冠肺炎大流行的集体耸人听闻的过程，正在解决有关疾病本身的尖锐而持久的科学不确定性，例如它如何传播，以及人们可以采取的最佳保护措施。这项建议应对这些挑战，以帮助公众了解和教育病毒传播和预防的科学知识--通过帮助提供准确和科学的数据和知识，以及值得信赖的专家的声音。该项目将使用人工智能、统计分析和专家编码来创建科学数据和专业知识的开放数据档案，因为随着时间的推移，这些数据和专业知识会在新闻和社交媒体上进行交流和更新。其目标是向公众提供有关病毒传播和预防科学的最新、准确的信息。该研究项目旨在了解科学知识、专业知识、数据和交流如何影响有关新出现的大流行的在线错误信息的传播和纠正。该项目团队正在调查信息如何在社交媒体平台上移动，以及如何在其他媒体平台上来回跳转，包括传统的新闻业--在线、印刷和广播渠道。它的目的是揭示与科学知识和专业知识有关的主张和统计如何塑造这些信息和影响动态，以及这些信息和影响动态是如何塑造的。方法论上的贡献包括在时间和安全关键的信息环境中扩大收集和分析社交媒体数据的基础设施，以及开发在科学专业知识的协调下通过大规模、动态的社交媒体数据聚合可变统计数据的技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Alongside the COVID-19 pandemic, people around the globe are experiencing an information ecosystem flooded with information—some accurate, some less so. Crisis events are inherently times of uncertainty and anxiety, and under those conditions, people try to make sense of the flood of information. This process, called collective sensemaking, has psychological and informational benefits, but it also makes us vulnerable to the spread of misinformation. The collective sensemaking process around the COVID-19 pandemic is addressing acute and persistent scientific uncertainty about the disease itself, such as how it spreads and the best protective actions people can take. This proposal addresses these challenges to help inform and educate the public about the science of virus transmission and prevention—by helping to surface accurate and scientific data and knowledge, alongside the voices of trustworthy experts. The project will use artificial intelligence, statistical analysis, and expert coding to create an open data archive of scientific data and expertise as it is communicated over time and updated in news and social media. The goal is to offer the public current, accurate information about the science of virus transmission and prevention.This research project seeks to understand how scientific knowledge, expertise, data, and communication affect the spread and correction of online misinformation about an emerging pandemic. The project team is investigating how information moves through social media platforms and jumps to and from other media platforms, including traditional journalism—online, print, and broadcast outlets. It aims to uncover how claims and statistics related to scientific knowledge and expertise shape, and are shaped by, these information and influence dynamics. Methodological contributions include expanding infrastructure for collecting and analyzing social media data in a time- and safety-critical information environment, and developing techniques for aggregating variable statistics, moderated by scientific expertise, over large-scale, dynamic social media data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.