RAPID: Dynamic Interactions between Human and Information in Complex Online Environments Responding to SARS-COV-2

RAPID：复杂在线环境中人与信息之间的动态交互，应对 SARS-COV-2

基本信息

批准号：
2028012
负责人：
Yan Wang
金额：
$ 8.2万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2021-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028012&HistoricalAwards=false
关键词：
RAPID Dynamic Interactions between Human

项目摘要

This Rapid Response Research (RAPID) project contributes to fundamental knowledge of risk, crisis communication and behavioral contagion in online environments during a large-scale pandemic outbreak (COVID-19) in the U.S. This project advances knowledge of how health and response agencies can better ensure credible information predominates in social media by quantitatively demonstrating the complex roles of social media in information diffusion during the U.S. COVID-19 pandemic response. Findings will aid in understanding how to reduce the risk of inappropriate behaviors (i.e. not practicing physical distancing) and preventable deaths due to mis- or disinformation and tools developed will enable time-critical tracking of the spreading of accurate and inaccurate information. These findings will support NSF's mission to promote the progress of science and to advance national health and well-being, especially during mission-critical circumstances of major health crises.The research project studies information and human response dynamics in communicating COVID-19 in an online environment, i.e. Twitter. The research identifies key influencers and misinformation sources and examines co-evolution in different information categories over time. Results will help population health agencies and stakeholders better understand how the strategic leveraging of credible information suppresses misinformation and can moderate its adverse consequences. Further, the project reveals how incongruous information may undermine community response goals. The research disentangles the interactive influences of communications between public health agencies, other governmental stakeholders, and the public by examining their social media activities, sentiments, and concerned topics in dynamic information flow networks. Findings will inform future risk communication strategies of virus transmission and prevention. The researchers use system dynamic modeling to investigate reference modes of COVID-19 specific communication. These techniques assess temporal trajectories of credible information and misinformation regarding epidemic-control communication, which in turn, informs the strategic coordination of future risk communication of complex mass casualty events and catastrophic health events such as virulent epidemics and global pandemics.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.

This Rapid Response Research (RAPID) project contributes to fundamental knowledge of risk, crisis communication and behavioral contagion in online environments during a large-scale pandemic outbreak (COVID-19) in the U.S. This project advances knowledge of how health and response agencies can better ensure credible information predominates in social media by quantitatively demonstrating the complex roles of social media in information diffusion during the U.S. COVID-19 pandemic response.调查结果将有助于理解如何降低不当行为的风险（即不进行身体距离）以及由于错误或虚假信息而导致的可预防的死亡，而所开发的工具将使时间关键地跟踪准确和不准确信息的传播。这些发现将支持NSF促进科学进步并促进国家健康和福祉的使命，尤其是在重大健康危机的关键任务情况下。研究项目研究信息和人类反应动态在在线环境中传达COVID-19，即Twitter。该研究确定了主要影响者和错误信息来源，并随着时间的推移研究了不同信息类别的共同进化。结果将有助于人口卫生机构和利益相关者更好地了解可靠信息的战略利用如何抑制错误信息并可以减轻其不利后果。此外，该项目揭示了不协调的信息如何破坏社区响应目标。该研究通过研究动态信息流网络中的社交媒体活动，情感和关注的主题，从而消除了公共卫生机构，其他政府利益相关者和公众之间通信的互动影响。调查结果将为未来的风险传播和预防传播风险沟通策略提供信息。研究人员使用系统动态建模来研究COVID-19特定交流的参考模式。这些技术评估了可信信息的时间轨迹和有关流行病控制的误导，这又为复杂大规模伤亡事件和灾难性健康事件的未来风险沟通的战略协调提供了依据，例如致命的流行病和全球大流行病等。这些奖项通过NSF的法定宣称和良好的评价来表现，并在广泛的范围内得到了良好的影响。标准。