# Using text analysis to quantify the similarity and evolution of scientific disciplines

arXiv: https://arxiv.org/abs/1706.08671 · HTML: https://mlsanity.com/paper/1706.08671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Quantifying the Dissimilarity of Texts](https://mlsanity.com/paper/2305.02457/) (2023)

## Abstract (arXiv)

We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g., computer science) are becoming increasingly central, but that on average the similarity between pairs has not changed in the last decades. This suggests that tendencies of convergence (e.g., multi-disciplinarity) and divergence (e.g., specialization) of disciplines are in balance.