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

调查和识别黑人和农村社区社交媒体上 COVID-19 错误信息曝光的异质性，以提供精准的公共卫生信息

• 批准号: 10707213
• 负责人: SHARATH CHANDRA GUNTUKU
• 金额: $ 78.57万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707213
• 关键词: Address; American; Attitude; Behavior; Black American; Black Populations; Black race; COVID-19; COVID-19 impact; COVID-19 pandemic; COVID-19 vaccination; Characteristics; Chronic Disease; Communities; Computer Analysis; Confusion; Consent; Consumption; County; Data; Data Set; Decision Making; Development; Disparity; Distress; Economics; Emergency Situation; Equity; Facebook; Future; Generations; Goals; HIV; HIV vaccine; Habits; Health; Health Campaign; Health Disparities Research; Health behavior; Healthcare; Heterogeneity; Human; Individual; Inequity; Institution; Interview; Knowledge; Language; Linguistics; Location; Longitudinal cohort; Machine Learning; Malignant Neoplasms; Masks; Maternal Mortality; Measures; Medicine; Mental Health; Methods; Minority Groups; Misinformation; National Institute on Minority Health and Health Disparities; Natural Language Processing; Noise; Outcome; Patients; Perception; Persuasive Communication; Politics; Predictive Analytics; Process; Public Health; Published Comment; Qualitative Methods; Race; Risk; Rural; Rural Community; Rural Population; Sampling; Signal Transduction; Social Distance; Source; Structural Racism; Structure; Surveys; Target Populations; Testing; Text; Training; Translating; Trust; Twitter; Urbanicity; Vaccination; Variant; caucasian American; combat; computer framework; computerized tools; current pandemic; design; disparities in morbidity; distrust; experience; flexibility; health communication; health disparity; health equity; individual response; infodemic; insight; lens; machine learning model; mortality; novel; pandemic disease; patient oriented; population health; predictive modeling; preference; prospective; racial disparity; racial diversity; rapid technique; recruit; response; rural Americans; rural area; rural dwellers; social; social media; support tools; synergism; theories; urban area

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.

项目摘要/摘要 在COVID-19大流行中，一种平行的“流行病”，大量可靠的信息和 不正确的错误信息，持续存在。错误信息交换和 消费主要在社交媒体平台上，威胁到个人和公共卫生。一个重要的 挑战仍然是制定在动态错误信息中检测可信赖和准确的“信号”的策略 “噪音。”这种错误信息导致对健康行为的混乱，不信任和困扰，例如 疫苗接种，戴面具和社交距离。发病率，死亡率，社会和 COVID-19的经济后果已得到充分记录；研究较少的是信息的变化 - 寻求和共同的199卫生决策针对黑人和农村社区。公共卫生 传统上，信息和运动依赖于基于理论的调查或访谈方法来衡量 知识和对设计健康消息传递的态度。社交媒体使用和并行进步的快速扩展 在机器学习中，分析提供了一个独特的机会，可以跟踪公众观点，知识和 同时态度将新颖的分析见解转化为精确的公共卫生 与黑人和农村社区的故意镜头进行沟通。该建议旨在建立 一个计算框架，以发现态度异质性和对共同信息的错误信息 19疫苗接种，高度引人入胜和有说服力的信息的模型预测指标（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 媒体错误的信息与种族（黑白个人）和位置（乡村和城市）的特定镜头进行了错误信息。 当我们将信息开发过程集中在COVID-19上，以及时且至关重要 行为，并比较四个特定受众的目标（黑色农村居民，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之间具有很高的适应性 可扩展到许多精确定位的受众。我们认为需要灵活和灵活 快速，以人为中心的内容生成的方法，支持准确，公平和有效的精度 公共卫生消息传递。由机器学习，预测分析和自然的计算工具 与以患者为中心的定性方法结婚的语言处理为传统提供了强大的协同作用 开展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 直接告知更广泛的公共卫生行动和未来策略，以便将其部署在当前 大流行和持续努力解决慢性疾病，艾滋病毒，癌症，孕产妇的种族差异 死亡率和心理健康。