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

项目总结 美国黑人的结直肠癌发病率和死亡率高于非西班牙裔 美国白人。虽然与儿童权利公约有关的一些差距有所减少(例如， )，在儿童权利公约筛查(CRC)的背景下，差异依然存在。研究表明，支持性和 信息丰富的社交网络，无论是线上还是线下，都可以改善美国黑人的社区合作伙伴关系。一个 越来越多的证据表明在线健康信息来源的重要性 扫描CRC和CRC，但对个人邮件的影响知之甚少 在这些平台上相遇。关于黑白信息的内容和数量的研究 美国人从在线健康信息来源遇到的情况仍然不清楚--特别是关于 在美国黑人寻求、扫描或共享哪些特定信息方面存在差异。那里 迫切需要了解哪些消息在特定疾病的高危人群中产生共鸣(例如， CRC)和可能对从事早期检测行为(例如，CRC)有顾虑的人，并可能面临 在接触网上公共信息方面存在差异。拟议的项目利用和应用了新的癌症 通信监测方法(例如，自然语言处理和众包)以审查 预防和控制结直肠癌的公共卫生沟通。目标1将使用计算的、自然的 语言处理方法，用于捕获和分析有关CRC和 CRC确定突出的信息、错误信息的来源和类型以及信息不平等。这 方法提供了一种高效、有效且响应迅速的方法来监控(Mis)信息和新出现的 有关CRC的消息。目标2将使用众包方法(维基调查)来评估人口 对关于社区合作中心的公共信息和信息的看法。招募具有全国代表性的样本 白人(N=1,000)和美国黑人(N=1,000)年龄在45-74岁之间，我们将使用创新的数据收集 称为维基调查的程序，将候选消息列为战略工作中的潜在消息目标 以推广合作伙伴关系。对于目标3，我们将进行随机对照消息试验(N=1,600)以确定 维基调查方法在为目标受众群选择消息方面的有效性。我们将使用 从研究2收集的数据，以确定针对每个目标具有较强论点的四组消息 受众排名：两个受众的最高评分消息，目标组内的最高评分消息， 目标组外最高级别的消息和中等/中等级别的消息。我们会越过那些 具有目标受众(美国白人/黑人)的消息类别，以测试消息是否通过维基选择 调查与未来坚持筛查建议和共享CRC的意向相关 留言。该项目将提供证据，帮助确定这些新方法的有效性和可扩展性， 这对今后创新形成性研究和评价方法是至关重要的。

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.