Quantifying the dynamics of topical fluctuations in language

TL;DR

This paper introduces the topical-cultural advection model to control for topical fluctuations in diachronic corpora, enabling more accurate analysis of language evolution and lexical innovation over two centuries.

Contribution

The paper presents a novel model for accounting for topical fluctuations in word frequency analysis, validated on historical and artificial language data.

Findings

The model effectively isolates genuine lexical change from topical effects.

Emergence of new words often aligns with rising trending topics.

The model provides a robust baseline for variability in diachronic language studies.

Abstract

The availability of large diachronic corpora has provided the impetus for a growing body of quantitative research on language evolution and meaning change. The central quantities in this research are token frequencies of linguistic elements in texts, with changes in frequency taken to reflect the popularity or selective fitness of an element. However, corpus frequencies may change for a wide variety of reasons, including purely random sampling effects, or because corpora are composed of contemporary media and fiction texts within which the underlying topics ebb and flow with cultural and socio-political trends. In this work, we introduce a simple model for controlling for topical fluctuations in corpora - the topical-cultural advection model - and demonstrate how it provides a robust baseline of variability in word frequency changes over time. We validate the model on a diachronic corpus spanning two centuries, and a carefully-controlled artificial language change scenario, and then use it to correct for topical fluctuations in historical time series. Finally, we use the model to show that the emergence of new words typically corresponds with the rise of a trending topic. This suggests that some lexical innovations occur due to growing communicative need in a subspace of the lexicon, and that the topical-cultural advection model can be used to quantify this.