Locating the Leading Edge of Cultural Change
November 22, 2024 ยท Declared Dead ยท ๐ Workshop on Computational Humanities Research
"No code URL or promise found in abstract"
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Authors
Sarah Griebel, Becca Cohen, Lucian Li, Jaihyun Park, Jiayu Liu, Jana Perkins, Ted Underwood
arXiv ID
2411.15068
Category
cs.CL: Computation & Language
Citations
3
Venue
Workshop on Computational Humanities Research
Last Checked
5 months ago
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.
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