# The Digital Information Environment of Lung Cancer and Lung Cancer Screening: Protocol for a Cross-Platform Social Media Content Analysis

## TL;DR

This study analyzes social media content about lung cancer and screening to understand misinformation, stigma, and decision-making factors that affect screening uptake.

## Contribution

The study introduces the first multiplatform, theory-driven analysis of lung cancer and screening content on social media.

## Key findings

- Social media content will be assessed for accuracy, stigma, and decision support across seven platforms.
- The study will identify patterns of stigma and nihilistic messaging specific to each platform.
- Findings will inform communication tools and digital interventions to improve lung cancer screening uptake.

## Abstract

Lung cancer screening (LCS) with low-dose computed tomography reduces mortality by up to 20%, yet uptake in the United States remains below 6% of eligible individuals. Factors contributing to low uptake include lack of awareness, eligibility confusion, stigma associated with smoking history, and nihilistic beliefs about outcomes. 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.

This study aims to (1) systematically characterize the clinical accuracy, stigma prevalence, and decision-support quality of lung cancer and screening content across 7 major social media platforms; (2) quantify platform-specific patterns in stigma manifestation and nihilistic messaging; (3) test whether inaccurate or stigmatizing content is associated with disproportionate engagement relative to accurate, nonstigmatizing content; and (4) as an exploratory aim, identify digital opinion leaders who could serve as partners for evidence-based dissemination.

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 2 content domains: LCS and lung cancer narratives (diagnosis, treatment, survivorship). The sampling strategy combines relevance-based sampling, targeting approximately 700‐1000 unique posts after deduplication—a sample size providing 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. All posts will be dual-coded by trained coders. 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.

Data collection began in October 2025 and is projected to be complete by July 2026. As of March 2026, data have been collected from 181 posts across 7 platforms. Results are expected to be published by December 2026. 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.

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.