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

• 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

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.

该快速响应研究 (RAPID) 项目有助于了解美国大规模流行病爆发 (COVID-19) 期间在线环境中的风险、危机沟通和行为传染的基础知识。该项目通过定量展示美国 COVID-19 大流行应对期间社交媒体在信息传播中的复杂作用，增进了卫生和应对机构如何更好地确保社交媒体中可信信息占主导地位的知识。研究结果将有助于了解如何减少不当行为（即不保持身体距离）的风险以及因错误或虚假信息而导致的可预防的死亡，所开发的工具将能够对准确和不准确信息的传播进行时间紧迫的跟踪。这些发现将支持 NSF 促进科学进步、促进国民健康和福祉的使命，特别是在重大健康危机的关键任务情况下。该研究项目研究在在线环境（即 Twitter）中传播 COVID-19 的信息和人类反应动态。该研究确定了关键影响因素和错误信息来源，并研究了不同信息类别随时间的共同演化。结果将帮助人口卫生机构和利益相关者更好地了解如何战略性地利用可信信息来抑制错误信息并减轻其不利后果。此外，该项目揭示了不一致的信息如何可能破坏社区响应目标。该研究通过检查动态信息流网络中的社交媒体活动、情绪和相关主题，理清了公共卫生机构、其他政府利益相关者和公众之间沟通的互动影响。研究结果将为未来病毒传播和预防的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 特定通信的参考模式。这些技术评估有关流行病控制通信的可信信息和错误信息的时间轨迹，从而为未来复杂的大规模伤亡事件和灾难性健康事件（例如致命流行病和全球大流行病）的风险通信的战略协调提供信息。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持 标准。

项目成果

Investigating dynamic relations between factual information and misinformation: Empirical studies of tweets related to prevention measures during COVID‐19

• DOI: 10.1111/1468-5973.12385
• 发表时间: 2021-02
• 期刊: Journal of Contingencies and Crisis Management
• 影响因子: 3.1
• 作者: Yan Wang;Shangde Gao;Wenyu Gao

Assessing the impact of geo-targeted warning messages on residents’ evacuation decisions before a hurricane using agent-based modeling

使用基于代理的建模评估飓风前地理定位警告消息对居民疏散决策的影响

• DOI: 10.1007/s11069-021-04576-1
• 发表时间: 2021
• 期刊: Natural Hazards
• 影响因子: 3.7
• 作者: Gao, Shangde;Wang, Yan
• 通讯作者: Wang, Yan

Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19

占主导地位的可信信息能否抑制危机中的错误信息？

• 发表时间: 2021
• 期刊: ArXivorg
• 作者: Wang, Yan;Gao, Shangde;Gao, Wenyu.
• 通讯作者: Gao, Wenyu.

Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics.

使用系统动力学对美国卫生机构在 Twitter 上的消息传播以及用户接触疫苗相关错误信息的情况进行建模。

• 发表时间: 2021
• 期刊: ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management
• 作者: Gao, Shangde;Wang, Yan;Platt, Lisa.
• 通讯作者: Platt, Lisa.