Combining Social Network and Semantic Content Analysis to Improve Knowledge Translation in Online Communities of Practice

Abstract

Establishing online communities of practice is an important part of the knowledge translation process in the modern healthcare system, but these online communities are new entity that is inherently different from traditional communities of practice that are dependent on existing social structures. The objective of this thesis is to combine communication analysis and content analysis to delve deeper into the communications within an online community to try and determine how online communities exist, and how that information can be leveraged to improve online knowledge translation. Using a novel approach this project will map the contents of online conversations to a structured medical lexicon (MeSH), and then use the inherent relationships of that lexicon to calculate term, user and thread similarities within an online community. These similarities, combined with connection analysis results, will provide a much deeper understanding of how online communities function. The methods developed here will then be tested on two separate mailing lists, the Pediatric Pain Mailing List (PPML) and SURGINET, a mailing list of general surgeons.