Using Natural Language Processing and Crowdsourcing to Monitor and Evaluate Public Information and Communication Disparities about Colon Cancer Screening

使用自然语言处理和众包来监测和评估有关结肠癌筛查的公共信息和沟通差异

• 批准号: 10729069
• 负责人: Andy J King
• 金额: $ 47.47万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729069
• 关键词: Address; Adopted; Adult; Affect; Age; Age Years; Alcohols; Behavior; Belief; Black American; Cancer Control; Categories; Code; Colorectal Cancer; Communication; Communication Research; Data; Data Collection; Death Rate; Diagnosis; Disease; Disparity; Disparity population; Early Diagnosis; Environment; Evaluation; Exposure to; Future; Goals; Guidelines; Health; Health Policy; Health Promotion; Human; Incidence; Individual; Inequality; Information and Media; Knowledge; Light; Malignant Neoplasms; Methods; Misinformation; Monitor; Natural Language Processing; Not Hispanic or Latino; Perception; Persons; Persuasive Communication; Population; Population Heterogeneity; Populations at Risk; Prevention; Procedures; Public Health; Public Opinion; Race; Randomized; Randomized, Controlled Trials; Recommendation; Research; Research Personnel; Respondent; Risk; Sampling; Scanning; Social Network; Source; Survey Methodology; Surveys; Techniques; Technology; Testing; Tobacco; United States; Woman; caucasian American; colon cancer screening; colorectal cancer prevention; colorectal cancer risk; colorectal cancer screening; crowdsourcing; design; digital media; efficacy trial; evidence base; experimental study; formative assessment; health communication; high risk; improved; innovation; men; novel; novel strategies; preference; racial population; recruit; screening; screening guidelines; social media; wiki

PROJECT SUMMARY Colorectal cancer (CRC) incidence and death rates are higher among Black Americans than non-Hispanic White Americans. While some CRC-related disparities have decreased (e.g., incidence and stage of presentation), disparities persist in the context of CRC screening (CRCS). Studies suggest that supportive and information-rich social networks, both online and offline, could improve CRCS among Black Americans. A growing body of evidence indicates the importance of online sources of health information seeking and scanning about CRC and CRCS, but little is known about the impact of the messages that individuals are encountering on these platforms. Research on the content and volume of messages White and Black Americans encounter from online health information sources is still unclear—particularly regarding any disparities that exist about what specific information is sought, scanned, or shared by Black Americans. There is a critical need to understand which messages resonate among populations at-risk for specific diseases (e.g., CRC) and who may have concerns about engaging in early detection behaviors (e.g., CRCS) and may face disparities in exposure to public information online. The proposed project utilizes and applies novel cancer communication surveillance approaches (e.g., natural language processing and crowdsourcing) to examine public health communication about CRC prevention and control. Aim 1 will use computational, natural language processing approaches to capture and analyze digital and social media information about CRC and CRCS to identify prominent messages, sources and types of misinformation, and information inequalities. This approach offers an efficient, effective, and responsive method to monitor (mis)information and emerging messages about CRCS. Aim 2 will use a crowdsourcing approach (wiki surveys) to assess population perceptions of public information and messages about CRCS. Recruiting nationally representative samples of White (N = 1,000) and Black American (N = 1,000) adults ages 45-74, we will use an innovative data collection procedure known as wiki surveys to rank candidate messages as potential message targets in strategic efforts to promote CRCS. For Aim 3, we will conduct a randomized controlled message trial (N = 1,600) to determine the validity of the wiki survey approach to selecting messages for targeted audience segments. We will use data collected from Study 2 to identify four sets of messages with strong arguments respective to each target audience’s rankings: highest rated messages for both audiences, highest rated messages for target in-group, highest rated messages for target out-group, and middle-/median-rated messages. We will cross those message categories with target audience (White/Black American) to test if messages selected via the wiki survey are associated with intentions to adhere to screening recommendations in the future and share CRCS messages. The project will offer evidence to help determine the validity and scalability of these novel methods, which is essential to innovate formative research and evaluation approaches in the future.

项目摘要 黑人美国人的结直肠癌（CRC）发病率和死亡率高于非西班牙裔 美国白人。尽管某些与CRC相关的差异有所改善（例如，事件和阶段 演示），在CRC筛查（CRC）的背景下差异持续存在。研究表明，支持和 信息丰富的社交网络在线和离线都可以改善黑人美国人中的CRC。一个 越来越多的证据表明，在线健康信息寻求和 扫描有关CRC和CRC的扫描，但对个人是信息的影响知之甚少 在这些平台上遇到。对消息的内容和量的研究白色和黑色 美国人从在线健康信息来源遇到的仍然尚不清楚 - 尤其是关于任何 黑人美国人感知，扫描或共享的特定信息的差异。那里 了解特定疾病的人群中哪些信息引起了共鸣的迫切需要（例如， CRC），谁可能担心从事早期检测行为（例如CRC），并且可能会面对 在线接触公共信息的差异。拟议的项目利用并应用了新型癌症 沟通监视方法（例如，自然语言处理和众包）以检查 关于CRC预防和控制的公共卫生沟通。 AIM 1将使用计算，自然 语言处理方法以捕获和分析有关CRC和的数字和社交媒体信息 CRC识别突出的消息，错觉的来源和类型以及信息不平等。这 方法提供了一种有效，有效且响应式的方法来监视（MIS）信息和新兴 有关CRC的消息。 AIM 2将使用众包方法（Wiki调查）来评估人口 对公共信息和有关CRC的消息的看法。招募全国代表性样本 白人（n = 1,000）和黑人（n = 1,000）45-74岁的成年人，我们将使用创新的数据收集 被称为Wiki调查的程序将候选消息作为战略努力中的潜在消息目标进行排名 促进CRC。对于AIM 3，我们将进行随机对照消息试验（n = 1,600）以确定 Wiki调查方法的有效性是为目标受众群体选择消息的有效性。我们将使用 从研究2收集的数据以识别四套与每个目标相对于每个目标的强烈论证的消息 受众的排名：两个受众的评分最高消息，目标内部的最高评分消息， 目标外部和中间/中位数消息的最高评分消息。我们会越过那些 带有目标受众（白人/黑人美国）的消息类别测试是否通过Wiki选择了消息 调查与未来遵守筛选建议并共享CRC的意图有关 消息。该项目将提供证据，以帮助确定这些新方法的有效性和可伸缩性， 这对于未来创新的形成性研究和评估方法至关重要。