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A Workflow for Analyzing Cultural Schemas in Texts

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  • Taylor, Marshall A.

    (New Mexico State University)

  • Stoltz, Dustin S.

    (Lehigh University)

Abstract

Concept class analysis (CoCA) is a method for recovering cultural schemas in texts using a combination of word embedding and community detection models. Like survey-based forms of schematic class analysis (SCA), however, interpreting results can be difficult. Some of these interpretive difficulties are applicable across types of SCA, while others are unique to CoCA. In this paper, we propose a complete workflow for interpreting and analyzing CoCA output. We use the case of social identity schemas in a collection of over 13,000 U.S. political blog posts to outline a number of interpretive and analytical strategies and a robustness check to make sense of the cultural schemas recovered from texts.

Suggested Citation

  • Taylor, Marshall A. & Stoltz, Dustin S., 2024. "A Workflow for Analyzing Cultural Schemas in Texts," SocArXiv zvwn2, Center for Open Science.
    • Handle: RePEc:osf:socarx:zvwn2
    DOI: 10.31219/osf.io/zvwn2
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    References listed on IDEAS

    as
    1. Alina Arseniev-Koehler & Jacob G. Foster, 2022. "Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat," Sociological Methods & Research, , vol. 51(4), pages 1484-1539, November.
    2. Taylor, Marshall A. & Stoltz, Dustin S., 2020. "Integrating Semantic Directions with Concept Mover's Distance to Measure Binary Concept Engagement," SocArXiv 36r2d, Center for Open Science.
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