JMIR Publications

ABSTRACT

Background:

Lung cancer screening with low-dose computed tomography of the chest reduces mortality by up to 20%, yet uptake in the U.S. remains below 6% of eligible individuals. Factors contributing to low uptake include lack of awareness, eligibility criteria confusion, stigma associated with smoking history, and nihilistic beliefs about outcomes. These constructs – stigma, nihilism, and misinformation – operate through distinct but interconnected pathways: stigma triggers shame-avoidance behaviors, nihilism undermines perceived screening benefit, and misinformation amplifies both by spreading inaccurate eligibility criteria and exaggerated harms. Social media increasingly shapes how individuals encounter health information, form risk perceptions, and make screening decisions. Because platform architectures differ in content modality, algorithmic curation, and user demographics, single-platform studies cannot reliably characterize the digital information environment or identify platform-specific intervention targets.

Objective:

This study aims to (1) systematically characterize the clinical accuracy, stigma prevalence, and decision-support quality of lung cancer and screening content across seven major social media platforms; (2) quantify platform-specific patterns in stigma manifestation and nihilistic messaging; (3) test whether inaccurate or stigmatizing content receives disproportionate algorithmic amplification; and (4) as an exploratory aim, identify digital opinion leaders who shape public discourse and could serve as partners for evidence-based dissemination.

Methods:

This cross-sectional content analysis will examine publicly accessible posts from Facebook, Instagram, TikTok, YouTube, X/Twitter, Reddit, and Bluesky. Posts will be identified through predefined search terms across two content domains: lung cancer screening and lung cancer narratives (diagnosis, treatment, survivorship). The sampling strategy combines relevance-based sampling, high-engagement sampling, and algorithmic recommendation sampling, targeting approximately 700-1,000 unique posts after deduplication – a sample size determined a priori to provide 80% power for cross-platform comparisons assuming medium effect sizes. A structured codebook operationalizing constructs from Diffusion of Innovations theory, attribution theory of stigma, and health misinformation frameworks will assess accuracy, stigma, decision support, and equity, with explicit theory-to-codebook mapping. All posts will be dual-coded by trained coders following a standardized protocol. Interrater reliability will be assessed using Gwet's AC1. Analyses will include descriptive statistics, cross-platform comparisons using chi-square and Kruskal-Wallis tests, and negative binomial regression models testing whether accuracy and stigma characteristics predict engagement.

Results:

Findings will characterize accuracy patterns, stigma prevalence, benefit-harm framing, and engagement dynamics across platforms, informing clinical communication tools, navigator training, and digital intervention development.

Conclusions:

This protocol describes the first multiplatform, theory-informed analysis of lung cancer and LCS content on social media. The study will generate foundational evidence to inform stigma-informed communication strategies, decision support tools, and equitable dissemination approaches. The methodology provides a replicable framework for monitoring health information ecosystems across disease contexts. The dual-coded dataset may inform future development of automated stigma detection tools, though such applications require separate validation. Clinical Trial: Not applicable