# Locating the Leading Edge of Cultural Change

arXiv: https://arxiv.org/abs/2411.15068 · HTML: https://mlsanity.com/paper/2411.15068/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Individuals, Institutions, and Innovation in the Debates of the French Revolution](https://mlsanity.com/paper/1710.06867/) (2017)
- [Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data](https://mlsanity.com/paper/1910.03233/) (2019)
- [Computational thematics: Comparing algorithms for clustering the genres of literary fiction](https://mlsanity.com/paper/2305.11251/) (2023)

## Built on by (most cited first, 2 of 2)

- [so much depends / upon / a whitespace: Why Whitespace Matters for Poets and LLMs](https://mlsanity.com/paper/2510.16713/) (2025)
- [A Data-driven Investigation of Euphemistic Language: Comparing the usage of "slave" and "servant" in 19th century US newspapers](https://mlsanity.com/paper/2503.15057/) (2025)

## Abstract (arXiv)

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.