Abstract

Public health surveillance is a critical part of understanding, and ultimately influencing, health behaviors. Traditional methods, such as questionnaires and focus groups have significant limitations including cost, delay, and size. Online social media data has the potential to overcome many of the challenges of traditional methods, but its exploitation is not trivial. We develop and apply computational techniques to enable public health surveillance in novel ways and on a larger scale than currently performed.In this regard, we present techniques for mining the who, what, and where of public health surveillance in social media. We show how computational methods can identify health content and conversations in social media, and that people do in fact speak openly about health topics, including those that might be considered private. In addition, we demonstrate how location information can be mined and used to study distributions of various conditions. Finally, and perhaps most importantly, we develop techniques to identify and leverage pertinent social network relationships in public health surveillance. We demonstrate each of these approaches in large data sets of actual social networks spanning blogs, micro-blogs, and video-sharing sites.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/