Grant Number:	4R37CA259156-05 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:	2025

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.




Associations between Anti-Gay Prejudice, Traditional Masculine Self-Concept, and Colorectal Cancer Screening-Related Outcomes among Black and White Men in the United States.
Authors: Chen T. , Wicke R. , King A.J. , Margolin D. , Chunara R. , Niederdeppe J. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2025-05-02 00:00:00.0; 34(5), p. 714-721.
PMID: 39945627
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Colorectal Cancer Racial Equity Post Volume, Content, and Exposure: Observational Study Using Twitter Data.
Authors: Tong C. , Margolin D. , Niederdeppe J. , Chunara R. , Liu J. , Jih-Vieira L. , King A.J. .
Source: Journal Of Medical Internet Research, 2025-02-03 00:00:00.0; 27, p. e63864.
EPub date: 2025-02-03 00:00:00.0.
PMID: 39899839
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Associations between news coverage, social media discussions, and search trends about celebrity deaths, screening, and other colorectal cancer-related events.
Authors: Liu J. , Niederdeppe J. , Tong C. , Margolin D. , Chunara R. , Smith T. , King A.J. .
Source: Preventive Medicine, 2024 Aug; 185, p. 108022.
EPub date: 2024-05-31 00:00:00.0.
PMID: 38823651
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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 00:00:00.0.
PMID: 37844803
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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, 2023-10-12 00:00:00.0; , .
EPub date: 2023-10-12 00:00:00.0.
PMID: 37838186
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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 00:00:00.0; 10(8), p. e37862.
EPub date: 2022-08-30 00:00:00.0.
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 00:00:00.0.
PMID: 36082150
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