title: Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data.
abstract: There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions.
authors.affiliation: 1 Indiana University, Bloomington, IN, USA.
authors.forename: Wendy R
authors.initials: WR
authors.lastname: Miller
authors.affiliation: 2 Indiana University-Purdue University Indianapolis, IN, USA.
authors.forename: Doyle
authors.initials: D
authors.lastname: Groves
authors.affiliation: 2 Indiana University-Purdue University Indianapolis, IN, USA.
authors.forename: Amelia
authors.initials: A
authors.lastname: Knopf
authors.affiliation: 2 Indiana University-Purdue University Indianapolis, IN, USA.
authors.forename: Julie L
authors.initials: JL
authors.lastname: Otte
authors.affiliation: 2 Indiana University-Purdue University Indianapolis, IN, USA.
authors.forename: Ross D
authors.initials: RD
authors.lastname: Silverman
keywords: Big Data; epilepsy; informatics; machine learning; methods