Locating the Leading Edge of Cultural Change

TL;DR

This paper evaluates different textual similarity measures to identify which best aligns with social evidence of cultural change across various corpora, highlighting the importance of focusing on impactful passages.

Contribution

It compares three text representations across diverse datasets to determine their effectiveness in capturing cultural change, emphasizing the significance of influential passages.

Findings

Highly-cited and younger authors lead in textual innovation.

No single text representation outperforms others consistently.

Focusing on top passages enhances alignment with social change evidence.

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.