Bridging qualitative and quantitative methods for user modeling: Tracing cancer patient behavior in an online health community

Zachary Levonian, Drew Richard Erikson, Wenqi Luo, Saumik Narayanan, Sabirat Rubya, Prateek Vachher, Loren Terveen, Svetlana Yarosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th International AAAI Conference on Web and Social Media, ICWSM 2020
PublisherAAAI press
Pages405-416
Number of pages12
ISBN (Electronic)9781577357889
StatePublished - 2020
Event14th International AAAI Conference on Web and Social Media, ICWSM 2020 - Atlanta, Virtual, United States
Duration: Jun 8 2020Jun 11 2020

Publication series

NameProceedings of the 14th International AAAI Conference on Web and Social Media, ICWSM 2020

Conference

Conference14th International AAAI Conference on Web and Social Media, ICWSM 2020
Country/TerritoryUnited States
CityAtlanta, Virtual
Period6/8/206/11/20

Bibliographical note

Funding Information:
We would like to thank the Minnesota Supercomputing Institute (MSI) at the University of Minnesota, our partners at CaringBridge, our colleagues in the GroupLens Research Laboratory, particularly Jasmine Jones and C. Estelle Smith, and the anonymous reviewers. This work was partially supported by NSF Grant No. 1464376.

Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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