The application of growth curve modeling for the analysis of diachronic corpora

Nini, Andrea; Corradini, Carlo; Guo, Diansheng; Grieve, Jack

doi:10.1163/22105832-00701001

Cited by 9 publications

(12 citation statements)

References 33 publications

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“…The regional dialect corpus used for this study consists of a large collection of geolocated Twitter data from the UK that we downloaded between 2014-01-01 and 2014-12-31 using the Twitter API. This data was collected as part of a larger project that has explored lexical variation on Twitter (see also Huang et al, 2016 ; Grieve et al, 2017 , 2018 ; Nini et al, 2017 ). In total, this corpus contains 1.8 billion words, consisting of 180 million Tweets, posted by 1.9 million unique accounts.…”

Section: Methodsmentioning

confidence: 99%

Mapping Lexical Dialect Variation in British English Using Twitter

Grieve

Montgomery

Nini

et al. 2019

Front. Artif. Intell.

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There is a growing trend in regional dialectology to analyse large corpora of social media data, but it is unclear if the results of these studies can be generalized to language as a whole. To assess the generalizability of Twitter dialect maps, this paper presents the first systematic comparison of regional lexical variation in Twitter corpora and traditional survey data. We compare the regional patterns found in 139 lexical dialect maps based on a 1.8 billion word corpus of geolocated UK Twitter data and the BBC Voices dialect survey. A spatial analysis of these 139 map pairs finds a broad alignment between these two data sources, offering evidence that both approaches to data collection allow for the same basic underlying regional patterns to be identified. We argue that these results license the use of Twitter corpora for general inquiries into regional lexical variation and change.

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Section: Methodsmentioning

confidence: 99%

Mapping Lexical Dialect Variation in British English Using Twitter

Grieve

Montgomery

Nini

et al. 2019

Front. Artif. Intell.

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“…The corpus analyzed in this study represents American Twitter and consists of 8.9 billion words of geo-coded American mobile Twitter data, totaling 980 million Tweets written by 7 million users from across the contiguous United States, posted and downloaded between October 11th, 2013, and November 22nd, 2014, using the Twitter API (http://dev.twitter.com) (see Huang et al 2016; Grieve et al 2017; Nini et al 2017). We focused on Twitter because this variety of language provides a uniquely large and accessible source of geo-coded and time-stamped natural language data.…”

Section: A Corpus Of American Tweetsmentioning

confidence: 99%

Mapping Lexical Innovation on American Social Media

Grieve

Nini²,

Guo

2018

Journal of English Linguistics

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In this paper, we introduce a method for mapping lexical innovation, which we then use to track the origin and spread of new words on American Twitter, based on a multi-billion-word corpus of Tweets collected between 2013 and 2014. We first extract fifty-four emerging words from the corpus by searching for words that are very uncommon at the end of 2013 but whose use rises dramatically over the course of 2014. We then map the origin and spread of each of these words. Based on these results, we identify five main regional patterns of lexical innovation on American Twitter, primarily associated with the West Coast, the Northeast, the Mid-Atlantic, the Deep South, and the Gulf Coast. We conclude by proposing explanations for these results and by discussing their significance to theories of language variation and change, including both the actuation and diffusion of lexical innovations.

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“…Language use on social media is informal and creative, which makes it a hotbed for lexical innovation. Recent work using Twitter data has focused, for example, on the identification of neologisms ( Grieve et al, 2018 ), on their geographical diffusion ( Eisenstein et al, 2014 ), and on trajectories of diffusion ( Nini et al, 2017 ). Empirical investigations on the basis of Reddit data include studies of the linguistic dissemination of neologisms ( Stewart and Jacob.…”

Section: Modelling and Measuring The Diffusion Of Lexical Innovationsmentioning

confidence: 99%

“…For linguists, social media provides large amounts of data of authentic language use which opens up new opportunities for the empirical study of language variation and change. The size of these datasets as well as their informal nature allow for large-scale studies on the use and spread of new words, for example, to gain insights about general trajectories of diffusion ( Nini et al, 2017 ) or about factors that influence whether new words spread successfully ( Grieve, 2018 ). Moreover, metadata about speakers facilitate the study of aspects of diffusion that go beyond what can be captured by usage frequency alone.…”

Section: Introductionmentioning

confidence: 99%

Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

Würschinger¹

2021

Front. Artif. Intell.

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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.

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The application of growth curve modeling for the analysis of diachronic corpora

Cited by 9 publications

References 33 publications

Mapping Lexical Dialect Variation in British English Using Twitter

Mapping Lexical Dialect Variation in British English Using Twitter

Mapping Lexical Innovation on American Social Media

Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter

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