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

Utah State Higher Education System--University Of Utah, Salt Lake City UT

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Abstract

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) and knowledge of certain risk factors (e.g., alcohol use). Studies suggest that supportive and information-rich social networks, both online and offline, could improve CRC outcomes among Black Americans. A growing body of evidence indicates the importance of online sources of health information seeking and scanning about CRC, 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 prevention (e.g., reduce alcohol use) and detection (e.g., CRCS) behaviors. 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. Extension Aim 1 will use computational approaches to capture and analyze digital and social media information about CRC. 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 artificial intelligence (AI)-generated messages about CRC. 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.