Abstract
The size and variation in both meaning-making and populations that characterize much contemporary text data demand research processes that support both discovery, interpretation and measurement. We assess one dominant strategy within the social sciences that takes a computer-led approach to text analysis. The approach is coined computational grounded theory. This strategy, we argue, relies on a set of unwarranted assumptions, namely, that unsupervised models return natural clusters of meaning, that the researcher can understand text with limited immersion and that indirect validation is sufficient for ensuring unbiased and precise measurement. In response to this criticism, we develop a framework that is computer assisted. We argue that our reformulation of computational grounded theory better aligns with the principles within grounded theory, anthropological theory generation and ethnography.
Originalsprog | Engelsk |
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Tidsskrift | Big Data and Society |
Vol/bind | 9 |
Udgave nummer | 1 |
Sider (fra-til) | 1-16 |
ISSN | 2053-9517 |
DOI | |
Status | Udgivet - jan. 2022 |
Bibliografisk note
Funding Information:The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Hjalmar Bang Carlsen was funded by the H2020 European Research Council (Grant number: 834540) as part of the project ‘The Political Economy of Distraction in Digitized Denmark’ (DISTRACT).
Publisher Copyright:
© The Author(s) 2022.
Emneord
- Computational text analysis
- interpretation
- mixed methods
- grounded theory
- digital social research