What Patients With Asthma Share When No One Listens: Multimethod Observational Study of Patient Narratives on Reddit (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».