JMIR Publications

ABSTRACT

Background:

Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including people with Type 1 diabetes (T1D).

Objective:

The current study attempts to document the major themes of Twitter posts using a natural language processes method to identify topics of interest in the T1D online community.

Methods:

Through Twitter scraping, we gathered a dataset of 691,691 Tweets from 8,557 accounts which represent people with T1D, their caregivers, health practitioners, and advocates. Tweet content was analyzed for sentiment and topic using Latent Dirichlet allocation. We used social network analysis to examine the degree to which identified topics are siloed within specific groups or disseminated through the broader T1D online community.

Results:

Tweets were, on average, positive in sentiment. Through topic modeling, we identified six broad bandwidth topics, ranging from clinical to advocacy to daily management to emotional health, which can inform researchers and practitioners interested in the needs of people with T1D. Moreover, social network analysis suggests that users are likely to see a mix of these topics discussed by accounts they follow.

Conclusions:

Twitter online communities are sources of information for people with T1D and members related to that community. Topics identified reveal key concerns of the T1D community and may be useful to practitioners and researchers alike. The methods used are efficient (low cost) while providing researchers with enormous amounts of data. We provide code to facilitate the use of these methods with other populations.