RAPID: Vulnerable Populations, Online Information, and COVID-19

如果弱势群体消费了关于新冠肺炎的不准确信息，他们的风险尤其大。该项目生成的数据和分析可用于更好地限制和控制针对弱势群体的关于新冠肺炎的不准确信息的传播。这些数据显示了在新冠肺炎危机最初爆发后，几个在线平台上不准确信息的数量和焦点发生了怎样的变化，以及这些信息是如何跨平台和在不同群体之间转移的。该项目审查了如何潜在地减轻有关大流行的不准确信息对公共卫生的影响，特别是寻求保护弱势群体中的个人不依赖不准确信息。该项目收集跨多个在线平台的在线小组的数据。使用人类和机器编码的组合，针对弱势群体的不准确信息的传播被记录在平台上。数学模型被用来理解不准确信息如何在网络中传播的动态，以及减少其传播的策略的相对潜在有效性。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。

Vulnerable populations are especially at-risk if they consume inaccurate information about COVID-19. This project generates data and analyses that can be used to better limit and control the spread of inaccurate information about COVID-19 that targets vulnerable populations. The data shows how the amount and focus of inaccurate information on several online platforms changed after the initial outbreak of the COVID-19 crisis, and how the information moved across platforms and among different groups. The project examines how to potentially mitigate the effects of inaccurate information about the pandemic on public health, and in particular seeks to protect individuals in vulnerable groups from relying on inaccurate information. This project collects data on online groups across multiple online platforms. Using a combination of human- and machine-coding, the diffusion of inaccurate information targeted to vulnerable groups across platforms is documented. Mathematical modeling is used to understand the dynamics of how inaccurate information diffuses across networks, as well as the relative potential effectiveness of strategies to reduce its spread.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.