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Computational Techniques for Public Health Surveillance

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.

Identiferoai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-4636
Date19 June 2013
CreatorsBurton, Scott H.
PublisherBYU ScholarsArchive
Source SetsBrigham Young University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations
Rightshttp://lib.byu.edu/about/copyright/

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