Abstract

Societies continually evolve and speakers use new words to talk about innovative products and practices. While most lexical innovations soon fall into disuse, others spread successfully and become part of the lexicon. In this paper, I conduct a longitudinal study of the spread of 99 English neologisms on Twitter to study their degrees and pathways of diffusion. Previous work on lexical innovation has almost exclusively relied on usage frequency for investigating the spread of new words. To get a more differentiated picture of diffusion, I use frequency-based measures to study temporal aspects of diffusion and I use network analyses for a more detailed and accurate investigation of the sociolinguistic dynamics of diffusion. The results show that frequency measures manage to capture diffusion with varying success. Frequency counts can serve as an approximate indicator for overall degrees of diffusion, yet they miss important information about the temporal usage profiles of lexical innovations. The results indicate that neologisms with similar total frequency can exhibit significantly different degrees of diffusion. Analysing differences in their temporal dynamics of use with regard to their age, trends in usage intensity, and volatility contributes to a more accurate account of their diffusion. The results obtained from the social network analysis reveal substantial differences in the social pathways of diffusion. Social diffusion significantly correlates with the frequency and temporal usage profiles of neologisms. However, the network visualisations and metrics identify neologisms whose degrees of social diffusion are more limited than suggested by their overall frequency of use. These include, among others, highly volatile neologisms (e.g., poppygate) and political terms (e.g., alt-left), whose use almost exclusively goes back to single communities of closely-connected, like-minded individuals. I argue that the inclusion of temporal and social information is of particular importance for the study of lexical innovation since neologisms exhibit high degrees of temporal volatility and social indexicality. More generally, the present approach demonstrates the potential of social network analysis for sociolinguistic research on linguistic innovation, variation, and change.

Highlights

  • IntroductionNew products and practices emerge, and speakers coin and adopt new words when they interact and share information

  • Societies continually evolve, new products and practices emerge, and speakers coin and adopt new words when they interact and share information

  • Unlike research on biological and cultural diffusion processes, sociolinguistic research has only recently been provided with data sources that are suitable for large-scale, data-based approaches which can rely on network analyses to study these phenomena empirically

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Summary

Introduction

New products and practices emerge, and speakers coin and adopt new words when they interact and share information. In a recent paper analysing contagion patterns of diseases in Nature Physics, Hébert-Dufresne et al (2020) suggest that the spread of viruses like SARS-CoV-2 follows principles of complex contagion through social reinforcement, and that it matches the dynamics of diffusion of cultural and linguistic innovations such as new words and internet memes. Does this confirm the widespread perception that new words ‘go viral’? Recent work has used Twitter data to investigate the geographical spread of lexical innovations (Eisenstein et al, 2014; Grieve et al, 2016), for example

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