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

项目概要 美国黑人结直肠癌 (CRC) 的发病率和死亡率高于非西班牙裔 美国白人。虽然一些与 CRC 相关的差异已经缩小（例如，CRC 的发生率和阶段） 介绍），但在 CRC 筛查 (CRCS) 方面仍然存在差异。研究表明，支持和 信息丰富的线上和线下社交网络可以改善美国黑人的 CRCS。一个 越来越多的证据表明，在线寻求和获取健康信息来源的重要性 扫描有关 CRC 和 CRCS 的信息，但人们对这些信息的影响知之甚少。 在这些平台上遇到。白与黑消息的内容和数量研究 美国人从在线健康信息来源中遇到的情况仍然不清楚，特别是关于任何 美国黑人寻求、扫描或分享哪些特定信息方面存在差异。那里 迫切需要了解哪些信息能在特定疾病高危人群中产生共鸣（例如， CRC）以及可能对参与早期检测行为（例如，CRCS）有顾虑并可能面临 在线公共信息的暴露程度存在差异。拟议的项目利用和应用新型癌症 通信监控方法（例如自然语言处理和众包）来检查 有关 CRC 预防和控制的公共卫生传播。目标 1 将使用计算、自然 用于捕获和分析有关 CRC 的数字和社交媒体信息的语言处理方法 CRCS 识别突出信息、错误信息的来源和类型以及信息不平等。这个 方法提供了一种高效、有效且响应迅速的方法来监控（错误）信息和新出现的信息 有关 CRCS 的消息。目标 2 将使用众包方法（维基调查）来评估人口 对有关 CRCS 的公共信息和消息的看法。招募具有全国代表性的样本 对于 45-74 岁的白人 (N = 1,000) 和美国黑人 (N = 1,000) 成年人，我们将使用创新的数据收集 称为维基调查的程序，将候选消息排名为战略工作中的潜在消息目标 推广CRCS。对于目标 3，我们将进行随机对照消息试验 (N = 1,600) 以确定 维基调查方法为目标受众群体选择消息的有效性。我们将使用 从研究 2 中收集的数据用于识别四组信息，这些信息对每个目标都有强有力的论据 受众排名：两个受众评分最高的消息，目标组内评分最高的消息， 目标外群体评价最高的消息，以及中等/中间评价的消息。我们将跨越那些 具有目标受众（美国白人/黑人）的消息类别，以测试是否通过 wiki 选择消息 调查与未来遵守筛查建议和分享 CRCS 的意图相关 消息。该项目将提供证据来帮助确定这些新方法的有效性和可扩展性， 这对于未来创新形成性研究和评估方法至关重要。

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.