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

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Abstract

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. 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. Through Twitter scraping, we gathered a data set of 691,691 tweets from 8557 accounts, which includes people with T1D, their caregivers, health practitioners, and advocates. 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. 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. 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.