Advice reification, learning, and emergent collective intelligence in online health support communities. (October 2019)

• Record Type: Journal Article
• Authors: Introne, Joshua; Goggins, Sean
• Is Part Of: Computers in human behavior. Volume 99(2019)
• Page Start: 205
• Page End: 218
• Publication Date: 2019-10
• Subjects: Online health community -- Health advice -- Community of practice -- Reification; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2019.05.028
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Online health support forums utilize straightforward online discussion designs to create a sociotechnical space where people can seek social support from others. The advice generated in these forums exists as an archival resource for future health information seekers. The present study uses mixed methods to investigate how invisible social processes lead advice to be adapted over time by forum members. Drawing on the construct of 'reification' from the communities of practice (COP) literature, we operationalize the reification of advice (RoA) as a process by which advice is developed across multiple discussion threads, and construct an algorithmic procedure to extract posts that trace this process. We evaluate our algorithm with crowd-workers, and perform an inductive, qualitative analysis to identify different modes of advice reification. We suggest that RoA could be used as the basis of a mid-level theory that treats online support communities and bundles of advice trajectories embedded in a shifting sociotechnical context. In our closing analysis, we propose that our approach might be a first step in an algorithmic procedure for assessing advice quality, drawing on the idea that reified advice may be considered a product of the collective intelligence of an online health support community. Highlights: Online health communities refine the advice they provide to newcomers over time. This is similar to the reification of practice in a community of practice. We introduce an algorithm to extract advice refinement arrayed across posts. We identify three different types of advice refinement. These different types reflect differences in the status of each type of advice for the community.