Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community

Abstract

Background Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including those with type 1 diabetes (T1D). There is some evidence that social media confers emotional and health-related benefits to people with T1D, including emotional support and practical information regarding health maintenance. Research on social media has primarily relied on self-reports of web-based behavior and qualitative assessment of web-based content, which can be expensive and time-consuming. Meanwhile, recent advances in natural language processing have allowed for large-scale assessment of social media behavior. Objective This study attempts to document the major themes of Twitter posts using a natural language processing method to identify topics of interest in the T1D web-based community. We also seek to map social relations on Twitter as they relate to these topics of interest, to determine whether Twitter users in the T1D community post in “echo chambers,” which reflect their own topics back to them, or whether users typically see a mix of topics on the internet. Methods Through Twitter scraping, we gathered a data set of 691,691 tweets from 8557 accounts, spanning a date range from 2008 to 2020, which includes 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 web-based community. Results Tweets were, on average, positive in sentiment. Through topic modeling, we identified 6 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. These analyses also replicate prior work using machine learning methods to map social behavior on the internet. We extend these results through social network analysis, indicating that users are likely to see a mix of these topics discussed by the accounts they follow. Conclusions Twitter 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.

Understanding the needs of people aging with type 1 diabetes: a user-focused research study with prototype diabetes advance directive and setting the stage for future research

“…Aside from technology, human assistance and labor also come into play as external resources, especially when material resources are limited. Not only do online diabetes community pages exist to serve as hubs for storytelling and network building [ 34 ], but they also serve as a digital space to access tangible resources. A participant once recalled their inability to access refills for insulin supply, in which they were able to find through a proximal source: a community member they met online who had an excess supply of insulin.…”

Selecting the Number and Labels of Topics in Topic Modeling: A Tutorial

“…Moreover, the ability for topic modeling to scale to thousands of documents facilitates its use in large data sets (Banks et al, 2018). For example, topic modeling has been used to study open-ended survey questions (Finch et al, 2018) and tweets in specific communities (Bedford-Petersen & Weston, 2021). Note that topic modeling is a good tool for uncovering broad subject-matter-based themes but not subtle nuances, which will still require human coding.…”

Social Network Analysis of Basic Necessity Scarcity on Twitter: Evidence from Indonesia

“…Social networking issues are examined through SNA, with past Twitter studies employing SNA in the fields of social science (Bahri & Widhyharto, 2021;Budi & Pamungkas, 2020;Sari et al, 2021;Tous-Rovirosa & Dergacheva, 2021), computer science (Alwafi, 2021;Morales-I-gras et al, 2021;Yang et al, 2021), medicine (Bedford-petersen & Weston, 2021;Moukarzel et al, 2021;Nazar & Pieters, 2021), engineering (Fani et al, 2020;Madani et al, 2020;Rathnayake, 2021) and business management and accounting (Battisti et al, 2021;Watanabe et al, 2021). From the analysis results on Scopus for Twitter, 276 articles published from 2006 to 2021 employed SNA.…”