{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:16:45Z","timestamp":1776122205197,"version":"3.50.1"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["834540"],"award-info":[{"award-number":["834540"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1177\/20539517221080146","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T08:28:42Z","timestamp":1647419322000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":42,"title":["Computational grounded theory revisited: From computer-led to computer-assisted text analysis"],"prefix":"10.1177","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2638-0932","authenticated-orcid":false,"given":"Hjalmar Bang","family":"Carlsen","sequence":"first","affiliation":[{"name":"Copenhagen Center for Social Data Science, University of Copenhagen"},{"name":"Department of Sociology, University of Copenhagen"}]},{"given":"Snorre","family":"Ralund","sequence":"additional","affiliation":[{"name":"Copenhagen Center for Social Data Science, University of Copenhagen"}]}],"member":"179","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"bibr1-20539517221080146","doi-asserted-by":"publisher","DOI":"10.17265\/2159-5313\/2016.09.003"},{"key":"bibr2-20539517221080146","doi-asserted-by":"publisher","DOI":"10.1002\/asi.23786"},{"key":"bibr3-20539517221080146","doi-asserted-by":"publisher","DOI":"10.1057\/9781137007285"},{"key":"bibr4-20539517221080146","unstructured":"Devlin J, Chang M-W, Lee K, et al. (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"bibr5-20539517221080146","doi-asserted-by":"publisher","DOI":"10.1177\/2053951715602908"},{"key":"bibr6-20539517221080146","doi-asserted-by":"crossref","unstructured":"DiMaggio P, Nag M, Blei D (2013) Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of u.s. government arts funding. 41(6): 570\u2013606. doi:10.1016\/j.poetic.2013.08.004. ISSN 0304-422X. 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