Investigating and identifying the heterogeneity in COVID-19 misinformation exposure on social media among Black and Rural communities to inform precision public health messaging

项目概要/摘要 在COVID-19大流行期间，一个平行的“信息流行病”，大量可靠的信息和 不准确的错误信息仍然存在。错误信息交流也显著增加， 消费，主要是在社交媒体平台上，这威胁到个人和公共健康。一个重要 挑战仍然是制定战略，在动态的错误信息中发现可信和准确的“信号” ”“噪音。“这种错误的信息导致了对健康行为的困惑、不信任和痛苦， 接种疫苗、戴口罩和保持社交距离。在发病率、死亡率、社会和经济方面的种族差异 COVID-19的经济后果有很好的记录;较少研究的是信息的变化- 寻求和COVID-19健康决策专门针对黑人和农村社区。公共卫生 信息和活动传统上依赖于基于理论的调查或访谈方法来衡量 设计健康信息的知识和态度。社交媒体使用的迅速扩大和平行发展 在机器学习中，分析提供了一个独特的机会来跟踪公众的观点、知识， 同时将新颖的分析见解转化为精确的公共卫生 用一个有意的透镜来观察黑人和农村社区。该提案旨在建立 一个计算框架，揭示对COVID的态度和错误信息暴露的异质性- 19疫苗接种，高度吸引人和有说服力的信息的模型预测器（包括来源，语言 选择和内容）;并使用务实的定性方法来了解个人对社会的反应， 媒体对种族（黑人和白色人）和地点（农村和城市）的特定透镜的错误信息。 虽然我们的信息开发过程侧重于COVID-19疫苗接种， 行为，并比较四个特定受众（黑人农村居民，白色农村居民， 黑人城市居民和白色农村居民），我们的方法在健康主题上具有高度适应性 并且可以扩展到许多精确定位的受众。我们认为需要灵活和敏捷的 快速、以人为本的内容生成方法，支持准确、公平和有效的精确度 公共卫生信息。由机器学习、预测分析和自然 语言处理与以患者为中心的定性方法结合， 公共卫生运动的方法，以确定和打击错误信息。这项研究的结果将 直接为更广泛的公共卫生行动和未来战略提供信息， 艾滋病毒/艾滋病、癌症、孕产妇死亡、艾滋病和疟疾等疾病方面的种族差异 死亡率和心理健康。

PROJECT SUMMARY/ ABSTRACT In the midst of the COVID-19 pandemic, a parallel `infodemic,' an abundance of reliable information and inaccurate misinformation, persists. There has also been a significant increase in misinformation exchange and consumption, largely on social media platforms, which threatens individual and public health. An important challenge remains to develop strategies to detect trusted and accurate `signals' amidst dynamic misinformation `noise.' This misinformation contributes to confusion, distrust, and distress around health behaviors such as vaccination, mask wearing, and social distancing. The racial disparities in morbidity, mortality, social, and economic consequences of COVID-19 are well documented; less studied are variations in the information- seeking and COVID-19 health decision-making specific to Black and rural communities. Public health information and campaigns have traditionally relied on theory-based surveys or interview methods to measure knowledge and attitudes to design health messaging. Rapid expansion of social media use and parallel advances in machine learning analytics provide a unique opportunity to track public views, knowledge, and attitudes simultaneously to translate novel analytic insights into precision public health communication with an intentional lens on Black and rural communities. This proposal aims to build a computational framework to uncover heterogeneity in attitudes and misinformation exposure towards COVID- 19 vaccination, model predictors of highly engaging and persuasive messages (including sources, linguistic choices, and content); and to use pragmatic qualitative methods to understand individual response to social media misinformation with a specific lens on race (Black and white individuals) and location (rural and urban). While we focus our message development process on COVID-19 vaccination as a timely and critical behavior, and compare targeting across four specific audiences (Black rural residents, white rural residents, Black urban residents, and white rural residents), our approach is highly adaptable across health topics and scalable to a number of precision-targeted audiences. We see a need for flexible and nimble methods for rapid, human-centered content generation that supports accurate, equitable, and effective precision public health messaging. Computational tools powered by machine learning, predictive analytics, and natural language processing married with patient-centered qualitative methods offer a powerful synergy to conventional approaches to public health campaigns to identify and combat misinformation. The findings from this study will directly inform broader public health action and future strategies so that they can be deployed in the current pandemic and in ongoing efforts to address racial disparities in chronic diseases, HIV, cancer, maternal mortality, and mental health.