RAPID; Information and Implications for Protection Motivation and Action During the COVID-19 Outbreak

迅速的;

• 批准号: 2026763
• 负责人: Hank Jenkins-Smith
• 金额: $ 20万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026763&HistoricalAwards=false
• 关键词: RAPID; Information; Implications; Protection; Motivation

The current spread of, and alarm about, the COVID-19 virus provides a unique and ephemeral opportunity to obtain meaningful time-series survey data on public beliefs, attitudes, behaviors, and the receipt of information of various kinds about the disease and its effects on taking protective action. The National Institute for Risk and Resilience (NIRR) utilizes its on-going Twitter data collection associated with coronavirus (collected since January 2020), and undertakes a series of monthly nation-wide surveys on public views to test the broader publics’ receipt of, trust in, and use of information about the virus posted on social media. The surveys will include questions about protective action behavior, trust in key actors, perceptions of risk associated with the outbreak, and perceptions of information accuracy/inaccuracy. The complementary survey and social media data streams will allow tracking the spread and penetration of information over time and as the disease spreads in order to match various narratives as they emerge on social media along with beliefs measured in the contemporaneous survey data. The time sensitive data will permit testing of hypotheses about the dynamic relationships between the spread of information in social media, broader public beliefs and behaviors, and effects on protective behaviors that may influence the spread of contagious diseases.The goal of this study is to measure and track the influence of information about the COVID-19 pandemic on Twitter among members of the broader US public. The study integrates two complementary streams of data to systematically examine the impact of information bubbles and various forms of information on protection motivation and actions in response to the COVID-19 outbreak in the US. First, since January 2020 ,the research team has collected all messages on Twitter that relate to COVID-19, by establishing a connection with the Twitter streaming API. The team obtains all posts and metadata that include any of the following key words: coronavirus, COVID-19, SARS-CoV-2, #coronavirus, #2019_nCov, and #COVID-19. From January 27 to Feb 24, the team collected more than 31 million different messages about the virus. The Twitter posts provide a continuous flow of data about the evolution of information networks and the promulgation and spread of information, but they do not provide information on the extent to which these factors are affecting protective motivations in the broader public and shaping the perceptions that drive them (such as trust in perceived risk). Second, the team collects online rolling nationwide surveys of the broader public’s understanding of COVID-19, with special attention to beliefs about the information that appears on Twitter, over the span of the next year. There are 10 nationwide surveys in all, one each month (time-series cross-sections), with collections timed to obtain 250 responses each week to increase the ability to quickly identify changes in beliefs, perceptions and associated protective behaviors. The surveys are designed to allow pairing the changing pattern of information of various sorts on social media with the receipt and belief of that information among the broader public. The experiments draw from the rise and spread of different kinds of information on Twitter.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.

Covid-19病毒的当前传播和警报提供了一个独特而短暂的机会，以获取有关公众信念，出席，行为的有意义的时间序列调查数据，并收到有关疾病及其对采取保护行动的影响的各种信息。美国国家风险与弹性研究所（NIRR）利用其与冠状病毒相关的持续的Twitter数据收集（自2020年1月以来收集），并就公众观点进行了一系列每月全国范围的调查，以测试更广泛的公众收到，信任和使用有关该病毒在社交媒体上发布的信息的信任和使用。这些调查将包括有关保护行为行为，对主要参与者的信任，与爆发相关的风险的看法以及信息准确性/不准确性的看法。完整的调查和社交媒体数据流将允许随着时间的流逝跟踪信息的传播和渗透，并且随着疾病的蔓延，以便在社交媒体上出现的各种叙述以及当代调查数据中的信念。时间敏感数据将允许测试有关信息在社交媒体中传播，更广泛的公众信仰和行为的动态关系以及对可能影响传染性疾病传播的保护行为的影响之间的动态关系。该研究的目的是衡量和跟踪有关广阔我们公众的Covid-19 pandectic of Covid-19的信息的影响。该研究集成了两个完整的数据流，以系统地检查信息气泡的影响以及各种形式的有关保护动机和行动的信息，以应对美国的Covid-19爆发。首先，自2020年1月以来，研究团队通过与Twitter流媒体API建立联系，在Twitter上收集了与Covid-19的所有消息。该团队获得了所有帖子和元数据，其中包括以下任何关键词：Coronavirus，Covid-19，Sars-Cov-2，#Coronavirus，＃Coronavirus，＃2019_NCOV和＃COVID-19。从1月27日至2月24日，团队收集了有关该病毒的3100万多个不同的信息。 Twitter帖子提供了有关信息网络演变以及颁布和信息传播的连续数据流，但它们没有提供有关这些因素影响更广泛的公众动机的程度的信息，并塑造了驱动它们的看法（例如对感知风险的信任）。其次，该团队收集了在线滚动国家调查，以了解广泛的公众对Covid-19的理解，并特别注意了明年在Twitter上出现的信息。总共有10项国家调查，每月一项（时间序列横截面），收集时间为每周获得250个回应，以提高快速识别信念，看法和相关保护行为的变化的能力。这些调查旨在使社交媒体上各种信息的信息模式与更广泛的公众之间的收到和信念相结合。该实验从Twitter上的各种信息的兴起和传播中得以汲取。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。