Slovotvir: Grassroots reform of the Ukrainian language reveals the role of drift and selection in language change

Abstract

Abstract Language change involves a complex interplay of selection and drift (or unbiased copying), shaping the popularity of words over time. This pre-registered study examines lexical evolution in the Ukrainian language using data from Slovotvir, a crowdsourcing platform where over 4,000 users proposed and ranked translations of foreign-origin words over 9 years. We rely on an agent-based model combined with novel generative inference methods to investigate the roles of frequency-dependent selection, selection for brevity, and random drift in shaping word popularity. Our results indicate that Slovotvir users exhibit a preference for shorter words, supporting Zipf’s principle of least effort. However, the popularity of translations, which we approximate using likes on the platform, appears largely frequency-independent, suggesting that users do not disproportionately favor already popular words. However, this conclusion might be limited by the fact that our data consist of low-frequency neologisms that are still evolving in this population of speakers. These findings contribute to a broader understanding of language change by providing empirical evidence that selection for brevity, rather than frequency-dependent selection, drives lexical evolution in this grassroots language reform initiative, which reflects and shaped real-world language change.