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Grant Details

Grant Number: 5R37CA259156-04 Interpret this number
Primary Investigator: King, Andy
Organization: University Of Utah
Project Title: Using Natural Language Processing and Crowdsourcing to Monitor and Evaluate Public Information and Communication Disparities About Colon Cancer Screening
Fiscal Year: 2024


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.


Making Sense of Social Media Data About Colorectal Cancer Screening.
Authors: King A.J. , Margolin D. , Tong C. , Chunara R. , Niederdeppe J. .
Source: Journal of the American College of Radiology : JACR, 2024 Apr; 21(4), p. 543-544.
EPub date: 2023-10-12.
PMID: 37838186
Related Citations

Global prevalence and content of information about alcohol use as a cancer risk factor on Twitter.
Authors: King A.J. , Dunbar N.M. , Margolin D. , Chunara R. , Tong C. , Jih-Vieira L. , Matsen C.B. , Niederdeppe J. .
Source: Preventive medicine, 2023 Dec; 177, p. 107728.
EPub date: 2023-10-14.
PMID: 37844803
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Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube.
Authors: Tong C. , Margolin D. , Chunara R. , Niederdeppe J. , Taylor T. , Dunbar N. , King A.J. .
Source: JMIR medical informatics, 2022-08-30; 10(8), p. e37862.
EPub date: 2022-08-30.
PMID: 36040760
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Missing the Bigger Picture: The Need for More Research on Visual Health Misinformation.
Authors: Heley K. , Gaysynsky A. , King A.J. .
Source: Science communication, 2022 Aug; 44(4), p. 514-527.
EPub date: 2022-08-05.
PMID: 36082150
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