JMIR Publications

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 aims 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 one-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 method analysis was conducted, comprising: a) Analysis using text lemmatization, b) structural topic modeling to identify topics based on word frequency, and c) sentiment and polarity analysis. This approach aimed to identify the most frequently used topics on Reddit, detect positive and/or negative sentiments based on the words used, and acceptance and/or rejection (polarity) based on the language used in the asthma subreddit. Statistical analyses were performed using R software (version 4.1.3), with a significance threshold set at p < 0.05.

Results:

After removing duplicates, a total of 7,806 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/emotions showed a stable trend throughout the year. Discrepancies in feelings/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 NLP, Bing, and NRC 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.