RAPID: #COVID-19: Understanding Community Response in the Emergence and Spread of Novel Coronavirus through Health Risk Communications in Socio-Technical Systems

迅速的：

基本信息

批准号：
2219618
负责人：
Arif Mohaimin Sadri
金额：
$ 7.94万
依托单位：
University of Oklahoma Norman Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-02-01 至 2023-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219618&HistoricalAwards=false
关键词：
RAPID #COVID 19 Understanding Community

项目摘要

Risk perception and risk averting behaviors of vulnerable communities in the emergence and spread of COVID-19 are spatio-temporal functions of individual or group interactions with their online social neighbors within or outside their communities and such interactions need to be captured through diverse information channels (e.g. traditional outlets such as radio, television, internet and/or non-traditional outlets such as social media). The primary goal of this Rapid Response Research (RAPID) project is to collect time-sensitive online social media and crowd-sourced data and analyze patterns of health-risk communication and community response in the emergence and spread of novel Coronavirus using data-driven methods and network science theories. The major focus will be towards understanding how individuals are socially influenced online, while communicating risk and interacting in their respective communities as the disease continues to spread. The notion of influence will be captured by quantifying the network effects on such communication behavior and characterizing how information is exchanged among people who are socially connected online and exposed to health risk in such outbreaks of disease. Given that communities responded to COVID-19 with limited or no preparation and there is uncertainty in the length of recovery for the communities already affected while new communities being threatened, the data collection effort requires rapid response for better coverage and careful monitoring. The data will include large-scale ephemeral online interactions of people in the affected communities and public officials who are involved in COVID-19 response, recovery, and mitigation efforts, followed by a data-driven network analytics and infographics of COVID-19 risk communication strategies and risk averting behaviors adopted. The proposed research will not only expand the knowledge base of spatio-temporal dynamics of risk perception and dissemination strategies in the emergence and aftermath of a major disease outbreak, but will also result in data-driven inference techniques to improve our understanding of how people express diverse concerns and how to harness and embed such information for designing intervention measures. The methodologies and findings of this rapid response research will benefit emergency management and public health agencies to define targeted information dissemination policies for public with diverse needs based on how people reacted to COVID-19 and their social network characteristics, activities, and interactions in response to similar public health hazards.Public engagement in risk communication can lead to more effective decision-making and enhanced public feedback to the regulatory process. The primary goal of this RAPID project is to mine and analyze large-scale time-sensitive perishable crowd-sourced and social media data (rich spatio-temporal data) and reveal patterns of health-risk communication and community response in the emergence and spread of novel Coronavirus using data-driven methods and network science theories. The specific aims are threefold: (1) to document how public interact and communicate health risk information through their online social networks during a major disease outbreak; (2) to authenticate data from multiple sources and detect anomalies to avoid information overload and spread of misinformation; and (3) to examine how online social networks influence protective actions (e.g., social distancing, self-quarantine decisions) i.e. information cascades in health risk communication. To achieve the goal and aims, the project will utilize ephemeral time and geo-tagged social media interactions of users, agencies, news sources supplemented with crowd-sourced information on COVID-19. This study will have five theoretical and methodological contributions to the literature. It will: (1) advance our understanding of how individuals are socially influenced online, while communicating health risks and interacting in their respective communities as the disease continues to spread; (2) inform the literature on how information is exchanged among people who are socially connected online and exposed to health risk in such outbreaks of disease; (3) use novel machine-learning and network science models to quantify influence and network effects on such communication behavior; (4) capture the variability in network composition, risk communication strategies and risk averting behaviors adopted based on spatio-temporal correlations of risk and disease contagion; (5) ensure authenticity of the collected data from multiple sources and develop more accurate fully-distributed computational algorithms tailored to health risk anomaly detection in socio-technical systems. The findings from this research will be useful to public health and emergency management agencies for tailoring effective information dissemination policies for diverse user groups based on their social network characteristics, activities, and interactions in response to similar public health hazards. The methodologies, and implications of this research can be transferred in designing effective intervention policies to other natural and man-made disaster contexts in which public health risks become major concerns. The project will engage, mentor, and offer an innovative active learning environment for K-12, undergraduate, and graduate students by giving priority to disadvantaged and underrepresented communities in USA. The project will train students on computational skills required for collecting, storing, processing, analyzing and modeling large-scale data using high performance computational resources.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.

在共同199的出现和传播中，易受伤害社区的风险感知和风险避免行为是个人或群体与其社区内部或外部与其在线社交邻居的互动的时空功能，需要通过多样化的信息渠道来捕获这种互动（例如，传统的媒体（例如，广播，互联网，互联网，互联网和非社交媒体），例如广播，互联网和非社交媒体，例如，例如，媒体和非社交媒体，例如，以下方面的媒体。这项快速响应研究（快速）项目的主要目标是收集时间敏感的在线社交媒体和众包数据，并分析使用数据驱动的方法和网络科学理论的新型冠状病毒的出现和传播中健康风险沟通和社区响应模式。主要重点是了解个人如何在网上受到社会影响，同时随着疾病的继续传播，在各自社区中传达风险并在各自社区中进行互动。影响概念将通过量化网络对这种通信行为的影响并表征在网上社交联系并在这种疾病爆发中暴露于健康风险的人们中如何交换信息的概念。鉴于社区对COVID-19的反应有限或没有准备，并且在受到威胁的新社区的恢复期限内有不确定性，因此数据收集工作需要快速响应，以更好地覆盖和仔细的监控。数据将包括参与COVID-19响应，恢复和缓解工作的受影响社区的人们和公职人员的大规模短暂在线互动，然后是数据驱动的网络分析和COVID-19风险通信策略的网络分析和信息图表，并避免采用风险行为。拟议的研究不仅将扩大风险感知和传播策略的时空动态的知识基础，并在发生重大疾病爆发的出现和后果，还将导致数据驱动的推理技术，以提高我们对人们如何表达各种关注以及如何利用和嵌入此类信息以设计干预措施的信息。这项快速响应研究的方法和发现将使应急管理和公共卫生机构受益，以根据人们对Covid-19的人们的反应及其社交网络特征，活动以及对类似公共卫生危害的相似公共健康危害的响应来定义具有多种需求的公共需求的有针对性的信息传播政策。在风险中，对类似的公共卫生危害的响应。风险沟通可能会导致更有效的公共决策和更高的公共反馈进程，并增强了公共往来的进取和增强对声音的反馈。这个快速项目的主要目标是挖掘和分析大规模的时间敏感的易腐烂众群体和社交媒体数据（丰富的时空数据），并使用数据驱动的方法和网络科学理论揭示了新颖冠状病毒的出现和传播中健康风险沟通和社区反应的模式。具体目的是三倍：（1）在重大疾病爆发期间通过其在线社交网络进行公众互动和传达健康风险信息；（2）验证来自多个来源的数据并检测异常，以避免信息过载和传播错误信息；（3）研究在线社交网络如何影响保护行为（例如，社会疏远，自我汇率决定），即健康风险交流中的信息级联。为了实现目标和目标，该项目将利用用户，代理商，新闻来源的杂物性社交媒体互动，并补充了有关Covid-19的人群信息。这项研究将对文献有五个理论和方法论贡献。它将：（1）提高我们对个人如何在网上受到社会影响的理解，同时随着疾病继续传播，传达健康风险并在各自社区中进行互动；（2）告知文献有关如何在网上社交联系并在这种疾病爆发中暴露于健康风险的人们中交换信息的文献；（3）使用新颖的机器学习和网络科学模型来量化对这种交流行为的影响和网络影响；（4）捕获基于风险和疾病传染的时空相关性采用的网络组成，风险交流策略和风险避免风险行为的可变性；（5）确保从多个来源收集的数据的真实性，并开发出针对社会技术系统中针对健康风险异常检测量身定制的完全分布的计算算法。这项研究的发现将对公共卫生和应急管理机构有用，可根据其社交网络特征，活动和互动，以应对类似的公共卫生危害，为各种用户群体量身定制有效的信息传播政策。这项研究的方法和含义可以转移到设计有效的干预政策到其他自然和人造灾难环境中，其中公共卫生风险成为主要问题。该项目将通过优先考虑美国的弱势群体和不足的社区，为K-12，本科和研究生提供创新的积极学习环境。该项目将使用高性能计算资源来培训学生收集，存储，处理，分析和建模大规模数据所需的计算技能。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Examining the Communication Pattern of Transportation and Transit Agencies on Twitter: A Longitudinal Study in the Emergence of COVID-19 on Twitter

检查 Twitter 上运输和过境机构的沟通模式：Twitter 上出现的 COVID-19 的纵向研究

DOI：
10.1177/03611981221082564
发表时间：
2022
期刊：
Transportation Research Record: Journal of the Transportation Research Board
影响因子：
0
作者：
Alam, Md Rakibul;Sadri, Arif Mohaimin
通讯作者：
Sadri, Arif Mohaimin

COVID-19: Understanding Construction Industry Responses on Twitter in the Emergence of Novel Coronavirus

COVID-19：了解新型冠状病毒出现时建筑行业在 Twitter 上的反应