Bridging Qualitative and Quantitative Methods for User Modeling: Tracing Cancer Patient Behavior in an Online Health Community

  • Zachary Levonian University of Minnesota
  • Drew Richard Erikson University of Minnesota
  • Wenqi Luo University of Minnesota
  • Saumik Narayanan University of Minnesota
  • Sabirat Rubya University of Minnesota
  • Prateek Vachher University of Minnesota
  • Loren Terveen University of Minnesota
  • Svetlana Yarosh University of Minnesota

Abstract

Researchers construct models of social media users to understand human behavior and deliver improved digital services. Such models use conceptual categories arranged in a taxonomy to classify unstructured user text data. In many contexts, useful taxonomies can be defined via the incorporation of qualitative findings, a mixed-methods approach that offers the ability to create qualitatively-informed user models. But operationalizing taxonomies from the themes described in qualitative work is non-trivial and has received little explicit focus. We propose a process and explore challenges bridging qualitative themes to user models, for both operationalization of themes to taxonomies and the use of these taxonomies in constructing classification models. For classification of new data, we compare common keyword-based approaches to machine learning models. We demonstrate our process through an example in the health domain, constructing two user models tracing cancer patient experience over time in an online health community. We identify patterns in the model outputs for describing the longitudinal experience of cancer patients and reflect on the use of this process in future research.

Published
2020-05-26
How to Cite
Levonian, Z., Erikson, D. R., Luo, W., Narayanan, S., Rubya, S., Vachher, P., Terveen, L., & Yarosh, S. (2020). Bridging Qualitative and Quantitative Methods for User Modeling: Tracing Cancer Patient Behavior in an Online Health Community. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 405-416. Retrieved from https://www.aaai.org/ojs/index.php/ICWSM/article/view/7310