Grant Number:	4R01CA279679-02 Interpret this number
Primary Investigator:	Walsh-Buhi, Eric
Organization:	Trustees Of Indiana University
Project Title:	Skin Cancer on Social Media: Analyzing Current Communications, Modeling Diffusion Potential, and Developing Innovative Prevention-Focused Messages
Fiscal Year:	2025

Exposure to ultraviolet light between ages 15 and 20 years is the most important etiological factor in skin cancer, yet adolescents and young adults (AYAs) this age are more likely to engage in health-compromising behaviors like indoor and outdoor tanning without skin protection. As an intervention modality, social media (SM) represents an opportunity to reach AYAs, who are among the most active Facebook, Instagram, and Twitter users. Characterizing the skin cancer communication landscape and creating effective risk/prevention posts, with an “understanding of which messages will resonate with specific groups” (The Surgeon General’s Call to Action to Prevent Skin Cancer), could enable targeted prevention methods for AYAs. Our long-term goal is to reduce health-compromising behaviors (e.g., indoor tanning, sunscreen nonuse) in at-risk AYAs, who are vulnerable to developing skin cancer. This proposal’s main objective is to: 1) characterize the SM landscape regarding skin cancer-related posts; and 2) develop/test theory-based messages for skin cancer prevention among AYAs that are clear, specific, consistent, and scientifically up to date. With a robust multidisciplinary team, we will accomplish this objective via 3 specific aims—AIM 1: Characterize skin cancer-related communication across three popular SM platforms. AIM 2: Build a predictive, explainable health communication model to determine the diffusion potential of skin cancer-related messages. AIM 3: Develop/pilot test theory-based sun-protection and indoor tanning-related messages for AYAs for future implementation and evaluation. In AIM 1 we will use content analyses to assess the skin cancer communication landscapes on Facebook, Instagram, and Twitter, describing message features, source characteristics, posters/users, and social networks. In AIM 2, we will apply machine learning methods to characterize message features; develop/evaluate a predictive model of a message diffusion potential using large-scale training data; apply the model to predict a set of online diffusion metrics for a given message; and assess its ability to reach skin cancer prevention-relevant populations. In AIM 3, we will engage 2 stakeholder segments in iterative rounds of message development/testing: 1) cancer organization staff (who post on their SM accounts); and 2) intended recipients of these messages: adolescents and young adults aged 15-20 years. The research is innovative because of its focus on posts across 3 SM platforms and the Multilevel Model of Meme Diffusion (M3D). This work will provide a strong basis for the future development of improved and targeted prevention efforts via digital platforms frequently used and easily accessed by AYAs, effectively supporting the NCI’s mission to “advance scientific knowledge and help all people live longer, healthier lives.”





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