Public Engagement with Lung Cancer Screening Information: Topic Modeling of Lung Cancer-Related Reddit Posts
Bibliographic record
Abstract
Lung cancer screening (LCS) with low-dose computed tomography is an effective strategy for early detection and improved survival. Despite its clinical benefits, public engagement with LCS topic remains unclear, particularly in the digital health communities. This study examines the thematic landscape of lung cancer-related discussions on Reddit. Using Python's Reddit API Wrapper, we collected 109,868 posts from six lung cancer-related subreddits between January 2019 and December 2024. After preprocessing, 105,118 unique posts were analyzed using Latent Dirichlet Allocation topic modeling to identify emergent themes. Topics were qualitatively reviewed and categorized into four high-level themes: treatment, mental health, smoking, and LCS. Mental health (71.82%) and treatment (16.84%) dominated the discourse, followed by smoking (8.30%), while LCS remained underrepresented (3.04%). Despite an increase in overall engagement from 2022 onward, LCS-related posts remained sparse, with no sustained upward trend. Reddit users frequently discuss treatment and mental health concerns related to lung cancer but rarely engage with LCS as a topic, revealing a critical gap in public awareness. These findings highlight the need for targeted public health strategies to promote LCS awareness on social media platforms, leveraging the platforms' growing role in health communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".