# Neutral evolution and turnover over centuries of English word popularity

## TL;DR

This study evaluates neutral evolution models against three centuries of English word frequency data, finding that a modified two-stage neutral model best explains observed vocabulary dynamics and turnover.

## Contribution

It introduces a two-stage neutral model that better replicates static and dynamic properties of language evolution compared to traditional models.

## Key findings

- Modified two-stage neutral model matches corpus data properties
- Traditional neutral models fail to replicate all properties simultaneously
- The model captures vocabulary turnover and frequency distribution dynamics

## Abstract

Here we test Neutral models against the evolution of English word frequency and vocabulary at the population scale, as recorded in annual word frequencies from three centuries of English language books. Against these data, we test both static and dynamic predictions of two neutral models, including the relation between corpus size and vocabulary size, frequency distributions, and turnover within those frequency distributions. Although a commonly used Neutral model fails to replicate all these emergent properties at once, we find that modified two-stage Neutral model does replicate the static and dynamic properties of the corpus data. This two-stage model is meant to represent a relatively small corpus (population) of English books, analogous to a `canon', sampled by an exponentially increasing corpus of books in the wider population of authors. More broadly, this mode -- a smaller neutral model within a larger neutral model -- could represent more broadly those situations where mass attention is focused on a small subset of the cultural variants.