Computational Approaches to Characterizing Online Health Communities
By: Shaodian Zhang
Publisher: [publisher not identified]
Published: 2016
Place of publication: [New York, N.Y.?]
Language: eng
Description: Online health communities (OHCs) have been increasingly popular among patients with chronic or life-threatening illnesses for the exchange of social support. Contemporary research of OHCs relies on methods and tools to handle analytics of massive user-generated content at scale to complement traditional qualitative analysis. In this thesis, we aim at advancing the area of research by providing computational tools and methods which facilitate automated content analysis, and by presenting applications of these tools to investigating member characteristics and behaviors. We first provide a framework of conceptualization to systematically describe problems, challenges, and existing solutions for OHCs from a social support standpoint, to bridge the knowledge gap between health psychology and informatics. With this framework in hand, we define the landscape of online social support, summarize current research progress of OHCs, and identify research questions to investigate for this thesis. We then build a series of computational tools for analyzing OHC content, relying on techniques of machine learning and natural language processing.

Leveraging domain-specific features, our tools are tailored to handle content analysis tasks on OHC text effectively. Equipped with computational tools, we demonstrate how characteristics of OHC members can be identified at scale in an automated fashion. In particular, we build up multi-dimensional descriptions for patient members, consisting of what topics they focus on, what sentiment they express, and what treatments they discuss and adopt. Patterns of how these member characteristics change through time are also investigated longitudinally. Finally, relying on computational analytics, members' behaviors of engagement such as debate and dropping-out are identified and characterized. Studies presented in this thesis discover static and longitudinal patterns of member characteristics and engagement, which are potential research hypotheses to be explored by health psychologists and clinical researchers. The thesis also contributes to the informatics community by making computational tools, lexicons, and annotated corpora available to facilitate future research.
Notes: Department: Biomedical Informatics.

Thesis advisor: Noemie Elhadad.

Thesis (Ph.D.)--Columbia University, 2016.