Governing PatientsLikeMe: information production and research through an open, distributed and data-based social media network

Abstract

Many organizations develop social media networks with the aim of engaging a wide range of social groups in the production of information that fuels their processes. This effort appears to crucially depend on complex data structures that allow the organization to connect and collect data from a myriad of local contexts and actors. One such organization, PatientsLikeMe, is developing a platform with the aim of connecting patients with one another while collecting self-reported medical data, which it uses for scientific and commercial medical research. Here the question of how technology and the underlying data structures shape the kind of information and medical evidence that can be produced through social media-based arrangements comes powerfully to the fore. In this observational case study, I introduce the concepts of information cultivation and social denomination to explicate how the development of such a data collection architecture requires a continuous exercise of balancing between the conflicting demands of patient engagement, necessary for collecting data in scale, and data semantic context, necessary for effective capture of health phenomena in informative and specific data. The study extends the understanding of the context-embeddedness of information phenomena and discusses some of the social consequences of social media models for knowledge making.