Analyzing lexical emergence in Modern American English online

Grieve, Jack; Nini, Andrea; Guo, Diansheng

doi:10.1017/s1360674316000113

Cited by 78 publications

(90 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specifically, we identified potential emerging word forms in the corpus following the procedure described in Grieve et al (2017). First, we extracted the word forms that occurred at least 500 times in the complete corpus, which amounted to 97,246 distinct types.…”

Section: Finding Emerging Wordsmentioning

confidence: 99%

Mapping Lexical Innovation on American Social Media

Grieve

Nini²,

Guo

2018

Journal of English Linguistics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Finding Emerging Wordsmentioning

confidence: 99%

Mapping Lexical Innovation on American Social Media

Grieve

Nini²,

Guo

2018

Journal of English Linguistics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lexical change online Language changes constantly, and one of the most notable forms of change is the adoption of new words (Metcalf, 2004), sometimes referred to as lexical entrenchment (Chesley and Baayen, 2010). New nonstandard words may arise through the mutation of existing forms by processes such as truncation (e.g, favorite to fave; Grieve et al, 2016) and blending (e.g., web+log to weblog to blog; Cook and Stevenson, 2010). The fast pace and interconnected nature of online communication is particularly conducive to innovation, and social media provides a "birds-eye view" on the process of change (Danescu-Niculescu-Mizil et al, 2013;Kershaw et al, 2016;Tsur and Rappoport, 2015).…”

Section: Related Workmentioning

confidence: 99%

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

Stewart

Eisenstein

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

In an online community, new words come and go: today's haha may be replaced by tomorrow's lol. Changes in online writing are usually studied as a social process, with innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the grammatical system in which it takes part. To investigate the role of social and structural factors in language change, we undertake a large-scale analysis of the frequencies of nonstandard words in Reddit. Dissemination across many linguistic contexts is a predictor of success: words that appear in more linguistic contexts grow faster and survive longer. Furthermore, social dissemination plays a less important role in explaining word growth and decline than previously hypothesized.

show abstract

“…In our previous research on lexical innovation, we identified 54 emerging words in a 9 billion word corpus of geocoded American Twitter collected between October 2013 and November 2014 (Grieve et al 2017. These 54 emerging words, which are listed in Table 1, grew steadily in popularity on Twitter in 2014 and represent a wide range of different parts-of-speech, semantic domains, and word formation processes.…”

Section: Discussionmentioning

confidence: 99%

“…For example, based on an analysis of a 9-billion-word corpus of American Twitter, we identified 54 newly emerging words, which were very uncommon at the end of 2013 but whose usage increased substantially over the course of 2014 (Grieve et al 2017;Grieve et al 2018). By extracting a relatively large sample of emerging words, we have been able to make general claims about the form, function, and origin of new word formations in Modern American English.…”

Section: Introductionmentioning

confidence: 99%

Natural selection in the modern english lexicon

Grieve¹

2018

Proceedings of the 12th International Conference on the Evolution of Language (Evolang12)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Analyzing lexical emergence in Modern American English online

Cited by 78 publications

References 38 publications

Mapping Lexical Innovation on American Social Media

Mapping Lexical Innovation on American Social Media

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

Natural selection in the modern english lexicon

Contact Info

Product

Resources

About