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

## TL;DR

This paper employs an information-theoretic linguistic similarity measure to analyze the organization and evolution of scientific disciplines over three decades, revealing insights into their convergence, divergence, and structural relationships.

## Contribution

It introduces a novel linguistic similarity metric and provides a comprehensive analysis of scientific field evolution, highlighting differences from citation-based classifications.

## Key findings

- Linguistic similarity correlates with but differs from expert classifications.
- Computer science has become increasingly central among disciplines.
- Overall similarity between disciplines has remained stable over decades.

## Abstract

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.