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 language||English (US)|
|Title of host publication||Proceedings of the 14th International AAAI Conference on Web and Social Media, ICWSM 2020|
|Number of pages||12|
|State||Published - 2020|
|Event||14th International AAAI Conference on Web and Social Media, ICWSM 2020 - Atlanta, Virtual, United States|
Duration: Jun 8 2020 → Jun 11 2020
|Name||Proceedings of the 14th International AAAI Conference on Web and Social Media, ICWSM 2020|
|Conference||14th International AAAI Conference on Web and Social Media, ICWSM 2020|
|Period||6/8/20 → 6/11/20|
Bibliographical noteFunding 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.
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