What Patients With Asthma Share When No One Listens: Multimethod Observational Study of Patient Narratives on Reddit (Preprint)
Bibliographic record
Abstract
BACKGROUND The use of social media platforms, such as Reddit, to seek and share information about disease management and treatment strategies is increasingly common. In the context of asthma—a chronic condition characterized by limiting symptoms and exacerbations that require active patient engagement and adherence to treatment—there is a lack of research describing the content of Reddit posts and the specific topics of interest to patients. OBJECTIVE This study aimed to describe the topics discussed by users on the Reddit asthma forum and identify the sentiments and polarity of the language used in the posts. METHODS A retrospective observational study of public posts on the asthma subreddit forum (r/Asthma) over a 1-year period (October 2023-October 2024). All posts and related threads were included, subdivided into hot, news, and top, and those voted “up” or “down,” those that received “awards,” categorized as “golds.” The messages were reviewed manually and excluded if they were not related to asthma. A mixed methods analysis was conducted, comprising (1) analysis using text lemmatization, (2) structural topic modeling to identify topics based on word frequency, and (3) sentiment and polarity analysis. This approach aimed to identify the most frequently used topics on Reddit, detect positive and negative sentiments based on the words used, and acceptance or rejection (polarity) based on the language used in the asthma subreddit. Statistical analyses were performed using R software (version 4.1.3; R Foundation for Statistical Computing), with a significance threshold set at P<.05. RESULTS After removing duplicates, 7806 posts were identified. The suitability of the chosen analysis model was confirmed, as it presented the best balance between exclusivity and semantic coherence. Clusters of 25 topics were identified and distributed according to their weight. The topics with the highest weight were Topic 7 (Symptoms and severity of asthma attacks) and Topic 18 (Causes of asthma). No significant differences were found in the evolution of emerging topics throughout the year except in Topic 20 (Seeking advice from people with asthma; P=.04), Topic 21 (Medical tests that should be reviewed periodically; P=.04), and Topic 22 (Times of year when attacks occur; P=.03). The proportion of feelings and emotions showed a stable trend throughout the year. Discrepancies in feelings and emotions were identified depending on the dictionaries used. Thus, a higher probability of positive feelings was confirmed in the AFINN lexicon. Meanwhile, negative feelings were significant in the Stanford Natural Language Processing, Bing, and National Research Council Canada lexicons. CONCLUSIONS These results can serve as a guide to identify hidden patient needs and help professionals develop specific interventions on topics relevant to patients.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".