Natural selection in the modern english lexicon

Abstract

This study tests the degree to which the form and function of 54 newly emerging words predicts their success over time in a multi-billion word corpus of American Twitter collected between 2013 and 2016. A linear model of the change in the relative frequency of each word is computed as a function of word length, part-of-speech, word formation process, and meaning. The analysis finds that the most important predictor of the success of these words is marking a new meaning. Shorter words and words created through standard word formation processes are also found to be more successful over time. These results are interpreted as supporting the theory that natural selection is the driving force behind lexical evolution.

Making “fetch” happen: The influence of social and linguistic context on nonstandard word growth and decline

“…Future work should also investigate more semantically-aware definitions of linguistic dissemination. The existence of semantic "neighbors" occurring in similar contexts (e.g., the influence of standard intensifier very on nonstandard intensifier af ) may prevent a new word from reaching widespread popularity (Grieve, 2018).…”

Lexical Emergence on Reddit: An Analysis of Lexical Change on the “Front Page of the Internet”

“…Overall, they make the case for using largescale web-based corpora for the analysis of lexical change, which the study at hand complies with. Grieve [2018] further analyses the "survival chances" of the lexemes identified by Grieve et al [2017] over a longer time period and investigates which characteristics might contribute to their firm establishment. A similar attempt is made by Stewart & Eisenstein [2018], who try to predict word adoption on Reddit by early dissemination, and by Cole et al [2017], who relate word adaptation to community size on Reddit.…”

Dissemination Dynamics of Receding Words: A Diachronic Case Study of Whom

“… Grieve et al (2017) develop a procedure to identify emerging words in a corpus of 8.9 billion Twitter messages, based on initially low frequency and a high increase in frequency over a given time period. In a follow-up study, Grieve (2018) predicts the further success of 54 emerging words identified in Grieve et al (2017) as a function of word length, part-of-speech, underlying word-formation process, and novelty of the word’s referent. The latter predictor is shown to be particularly relevant in determining the frequency development of innovative words, whereas part-of-speech does not appear to play a significant role.…”