Study of dynamic communities on networks, diabetes tweets 2013-2014


This data collection consists of tweets in English that contain the term 'diabetes' posted between March 2013 and January 2014. Abstract from the paper: Social media are being increasingly used for health promotion. Yet the landscape of users and messages in such public fora is not well understood. So far, studies have typically focused either on people suffering from a disease, or on agencies that address it, but have not looked more broadly at all the participants in the debate and discussions. We study the conversation about diabetes on Twitter through the systematic analysis of a large collection of tweets containing the term 'diabetes', as well as the interactions between their authors. We address three questions: (1) what themes arise in these messages?; (2) who talks about diabetes and in what capacity?; and (3) which type of users contribute to which themes? To answer these questions, we employ a mixed-methods approach, using techniques from anthropology, network science and information retrieval. We find that diabetes-related tweets fall within broad thematic groups: health information, news, social interaction, and commercial. Humorous messages and messages with references to popular culture appear constantly over time, more than any other type of tweet in this corpus. Top 'authorities' are found consistently across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as stockmarket-listed companies with no specific diabetes expertise. These authorities fall into seven interest communities in their Twitter follower network. In contrast, the landscape of 'hubs' is diffuse and fluid over time. We discuss the implications of our findings for public health professionals and policy makers. Our methods are generally applicable to investigations where similar data are available.This project is concerned with the study of the evolution of narratives in online social media, and the identification of the relevant actors who have an outsize influence on the conversations.

Data collected using Twitter Gnip PowerTrack API

Metadata Access
Creator Beguerisse Diaz, M, University of Oxford
Publisher UK Data Service
Publication Year 2016
Funding Reference James S. McDonnell Foundation
Rights Mariano Beguerisse Diaz, University of Oxford. Mauricio Barahona, Imperial College London. Amy K McLennan, University of Oxford. Stanley Ulijaszek, University of Oxford. Guillermo Garduño, Sinnia. Twitter; The Data Collection is available to any user without the requirement for registration for download/access.
OpenAccess true
Resource Type Text
Discipline Social Sciences
Spatial Coverage Worldwide; United Kingdom; Mexico