Inferring Leadership Structure from Data on a Syntax Change in English

Abstract

In a typical human population, some features of the lan- guage are bound to be in flux. Variation in each individual's usage rates of optional features reflects language change in progress. Sociolinguistic sur- veys have determined that some individuals use new features to a greater degree than the population average, that is, they seem to be leading the change. This article describes a mathematical model of the spread of lan- guage change inspired by a model from population genetics. It incorporates the premise that some individuals are linguistic leaders and exert more in- fluence on the speech of learning children than others. Using historical data from the rise of do-support in English, a maximum likelihood calculation yields an estimate for the influence ratio used in the model. The influence ratio so inferred indicates that 19 of the 200 simulated individuals account for 95% of the total influence, confirming that language change may be driven by a relatively small group of leaders. The model can be improved in any number of ways, but additional features must be selected carefully so as not to produce a computationally intractable inference problem. This project demonstrates how data and techniques from different subfields of linguistics can be combined within a mathematical model to reveal other- wise inaccessible information about language variation and change.