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.

这个快速反应研究（RAPID）项目有助于对风险的基本知识，大规模流行病爆发期间在线环境中的危机沟通和行为传染（2019冠状病毒病）该项目通过定量展示社交媒体在信息传播过程中的复杂作用，促进了卫生和反应机构如何更好地确保可信信息在社交媒体中占主导地位的知识。美国COVID-19疫情应对。调查结果将有助于了解如何减少不当行为（即不进行物理距离）的风险，以及由于错误或虚假信息而导致的可预防死亡，开发的工具将能够及时跟踪准确和不准确信息的传播。这些发现将支持NSF的使命，即促进科学进步，促进国民健康和福祉，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。该研究确定了关键的影响者和错误信息来源，并研究了随着时间的推移，不同信息类别的共同演变。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良后果。此外，该项目揭示了不一致的信息可能会破坏社区的反应目标。该研究通过考察公共卫生机构、其他政府利益相关者和公众在动态信息流网络中的社交媒体活动、情绪和关注话题，解开了公共卫生机构、其他政府利益相关者和公众之间沟通的互动影响。研究结果将为未来的病毒传播和预防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特定传播的参考模式。这些技术评估可信信息的时间轨迹和关于导弹控制通信的错误信息，这反过来，为未来复杂的大规模伤亡事件和灾难性健康事件（如致命流行病和全球流行病）的风险沟通提供战略协调。该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.