Valerie L. Shalin (Advisor), Gary Burns (Committee Member), Joseph Houpt (Committee Member), Amit Sheth (Committee Member)
Doctor of Philosophy (PhD)
This dissertation considers what it means to think differently, using naturalistic verbal evidence. This problem is inspired by a gap within the Wisdom of the Crowd (WoC) literature, but relevant to the study of language processes, mental models, and the vast emerging resource of social media data. I propose a methodological framework to characterize diversity of thought through the quantification of social media data. Four stages of research considered: a) the properties of a sample domain, b) how to identify and select diagnostic content using classification methods, c) how to quantify qualitative content in order to categorize and compare individuals, and d) how to assess the relative merits and challenges of content classification methods, including whether differences in thought actually affect outcomes. The emphasis is on pervasive issues pertinent the analysis of unstructured verbal data, rather than the specific, albeit largely successful solutions explored. Such issues were identified when defining and applying the methodological framework, and generally indicate the influence of sample domain on process measures, success at higher levels of abstraction, and a lack of continuity between all levels of analysis.
Department or Program
Department of Psychology
Year Degree Awarded
Copyright 2019, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.