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

项目摘要/摘要 在新冠肺炎大流行中，另一个平行的“信息者”，大量可靠的信息和 不准确的错误信息仍然存在。错误信息的交流也显著增加， 消费，主要是在社交媒体平台上，这威胁到个人和公共健康。一个重要的 挑战仍然是制定策略，在动态错误信息中检测可信和准确的“信号” “有噪音。”这种错误的信息会导致围绕健康行为的困惑、不信任和痛苦，例如 接种疫苗、戴口罩和社交距离。发病率、死亡率、社会和社会方面的种族差异 新冠肺炎的经济后果是有据可查的，但对其中信息的变化研究较少。 寻求和新冠肺炎针对黑人和农村社区的健康决策。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。社交媒体使用的快速扩展和同步发展 在机器学习中，分析提供了跟踪公众观点、知识和 同时将新的分析性见解转化为精确的公共卫生的态度 以有意的视角对黑人和农村社区进行交流。这项提案旨在建立 一个计算框架，以揭示对COVID的态度和错误信息暴露的异质性- 19接种疫苗，高度吸引人和有说服力的信息的模型预测者(包括来源、语言 选择和内容)；并使用实用的定性方法来理解个人对社会的反应 带有特定视角的关于种族(黑人和白人)和地点(农村和城市)的媒体错误信息。 虽然我们将我们的信息开发过程集中在新冠肺炎疫苗接种上，认为这是及时和关键的 行为，并比较四个特定受众(黑人农村居民，白人农村居民， 黑人城市居民和白人农村居民)，我们的方法在健康主题上具有很强的适应性 并可扩展到许多精准目标受众。我们认为需要灵活和灵活的 支持准确、公平和有效的精确度的以人为中心的快速内容生成方法 公共卫生信息。由机器学习、预测分析和自然语言支持的计算工具 语言处理与以患者为中心的定性方法相结合，为传统的语言处理提供了强大的协同 公共卫生运动的方法，以识别和打击错误信息。这项研究的发现将 直接为更广泛的公共卫生行动和未来战略提供信息，以便在当前 在解决慢性病、艾滋病毒、癌症、孕产妇等方面的种族差异方面的持续努力 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.