Extracting information from S-curves of language change

TL;DR

This study investigates how the shape of S-curves in language change reveals the underlying factors driving innovation adoption, using historical data and models to distinguish endogenous and exogenous influences.

Contribution

It introduces a measure to quantify endogenous and exogenous factors in language adoption S-curves and applies it to real historical linguistic data.

Findings

Exogenous factors dominate in some language reforms.

Endogenous factors drive other language change processes.

S-curve shapes vary depending on the underlying adoption mechanism.

Abstract

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period, and slow end). In this paper, we analyze how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g., the Bass dynamics on complex networks) we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism. (published at "J. R. Soc. Interface, vol. 11, no. 101, (2014) 1044"; DOI: http://dx.doi.org/10.1098/rsif.2014.1044)