# Reliable Detection and Quantification of Selective Forces in Language Change

arXiv: https://arxiv.org/abs/2305.15914 · HTML: https://mlsanity.com/paper/2305.15914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 80 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 6 of 6)

- [Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution](https://mlsanity.com/paper/1501.00960/) (2015)
- [Evolutionary forces in language change](https://mlsanity.com/paper/1608.00938/) (2016)
- [The Dynamics of Norm Change in the Cultural Evolution of Language](https://mlsanity.com/paper/1809.04328/) (2018)
- [How individuals change language](https://mlsanity.com/paper/2104.10210/) (2021)
- [Internal and external dynamics in language: Evidence from verb regularity in a historical corpus of English](https://mlsanity.com/paper/1408.2699/) (2014)
- [Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model](https://mlsanity.com/paper/2303.04691/) (2023)

## Abstract (arXiv)

Language change is a cultural evolutionary process in which variants of linguistic variables change in frequency through processes analogous to mutation, selection and genetic drift. In this work, we apply a recently-introduced method to corpus data to quantify the strength of selection in specific instances of historical language change. We first demonstrate, in the context of English irregular verbs, that this method is more reliable and interpretable than similar methods that have previously been applied. We further extend this study to demonstrate that a bias towards phonological simplicity overrides that favouring grammatical simplicity when these are in conflict. Finally, with reference to Spanish spelling reforms, we show that the method can also detect points in time at which selection strengths change, a feature that is generically expected for socially-motivated language change. Together, these results indicate how hypotheses for mechanisms of language change can be tested quantitatively using historical corpus data.