Locating the Leading Edge of Cultural Change

  • Sarah Griebel
  • , Becca Cohen
  • , Lucian Li
  • , Jaihyun Park
  • , Jiayu Liu
  • , Jana Perkins
  • , Ted Underwood

Research output: Contribution to journalConference articlepeer-review

Abstract

Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (topic models, document embeddings, and word-level perplexity) to three different corpora (literary studies, economics, and fiction). In every case, works by highly-cited authors and younger authors are textually ahead of the curve. We don’t find clear evidence that one representation of text is to be preferred over the others. But alignment with social evidence is strongest when texts are represented through the top quartile of passages, suggesting that a text’s impact may depend more on its most forward-looking moments than on sustaining a high level of innovation throughout.

Original languageEnglish (US)
Pages (from-to)232-245
Number of pages14
JournalCEUR Workshop Proceedings
Volume3834
StatePublished - 2024
Event2024 Computational Humanities Research Conference, CHR 2024 - Aarhus, Denmark
Duration: Dec 4 2024Dec 6 2024

Keywords

  • bibliometrics
  • cultural change
  • document embeddings
  • fiction
  • topic modeling

ASJC Scopus subject areas

  • General Computer Science

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