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Machine Learning and the Analysis of Culture
Department of Sociology, University of Lucerne, Switzerland.
l'Ecole Polytechnique in Paris, France.ORCID iD: 0000-0003-3099-5240
2024 (English)In: The Oxford Handbook of Machine Learning and Sociology / [ed] Christian Borch and Juan Pablo Pardo-Guerra, London: Oxford University Press , 2024Chapter in book (Refereed)
Abstract [en]

The focus of this chapter is on how machine learning (ML) impacts the analysis of culture insociology. It shows how ML has greatly advanced the analysis of culture, with new tools enabling amassive and fine-grained extraction of information from textual and audiovisual troves as well as dataanalysis, operationalizing long-standing cultural sociology concepts. It also indicates that this renewedinterest is building on already fertile ground, as sociologists of culture have long used and reflected onformal models when analyzing culture. The chapter suggests that as the toolbox of ML approachesexpands, so will the need for methodological reflection on the datasets and algorithms used, analyzed,and interpreted. The chapter also suggests that ML techniques can serve as catalysts to generate newtheoretical insights. The chapter’s conclusion discusses the potential of ML research to generate newtheoretical insights abductively and advocates for methodological reflexivity.

Place, publisher, year, edition, pages
London: Oxford University Press , 2024.
Keywords [en]
culture, machine learning, topic modeling, word embeddings, large language models (LLMs), unsupervised and supervised models, frames, schema, text, sound, images, theory
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-207047ISBN: 9780199933815 (print)OAI: oai:DiVA.org:liu-207047DiVA, id: diva2:1893153
Available from: 2024-08-28 Created: 2024-08-28 Last updated: 2025-04-07Bibliographically approved

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Ollion, Etienne

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
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