Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community
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Date
2021-05-27
Authors
Bedford-Petersen, Cianna
Weston, Sara J.
Journal Title
Journal ISSN
Volume Title
Publisher
JMIR Publications
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.
Description
11 pages
Keywords
type 1 diabetes, diabetes community, social media, Twitter, natural language processing, diabetes community, network analysis, Latent Dirichlet Allocation, diabetes, data scraping, sentiment analysis
Citation
Bedford-Petersen C, Weston SJ Mapping Individual Differences on the Internet: Case Study of the Type 1 Diabetes Community JMIR Diabetes 2021;6(4):e30756. https://doi.org/10.2196/30756