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Human languages evolve on a cultural timescale: new linguistic variants are continually being innovated, some variants are selected for, and some are selected against. These processes of selection constitute the engine of language change and, on longer timescales, language evolution. Previous research on lexical competition and selection has generally been based on either limited subsets of natural data (Verkerk, Calude, & Pagel, 2014; Ahern, Newberry, Clark, & Plotkin, 2016; Calude, Miller, & Pagel, 2017) or simulations (Reali & Griffiths, 2010; Blythe & Croft, 2012; Stadler, Blythe, Smith, & Kirby, 2016). Identifying genuine instances of innovation and selection from natural language corpora on a broad scale is challenging. Language does not exist in a vacuum: discourse topics tend to reflect contemporary social, cultural and political issues. The relative frequency of a word at a given time period is therefore not necessarily an objective measure of its selective fitness at the time, but potentially the result of its related topic(s) being currently more discussed or reported on (cf. also Chelsey & Baayen, 2010; Lijffijt, Sily, & Nevalainen, 2012; Szmrecsanyi, 2016). Here we present the topical-cultural advection model, a method which allows us to measure and control for the rise and fall of discourse topics on the frequencies of individual elements (e.g., words).

Identifying linguistic selection and innovation while controlling for cultural drift