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

Stewart, Ian; Eisenstein, Jacob

doi:10.18653/v1/d18-1467

Cited by 18 publications

(39 citation statements)

References 40 publications

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“…8 Furthermore, it is interesting to note that the frequency-based as well as the dissemination-based measures are considerably clumped together in feature importance space for absolute growth δ a , with the frequency-based predictors topping the dissemination-based ones. This is in line with recent work on the relative importance of frequency and social dissemination in lexical change (Stewart and Eisenstein, 2018). Higher values in these features correlate with a higher likelihood of growth, except for topicality: here, growth is more likely with lower topicality, which as discussed above indicates higher topical dissemination.…”

Section: Resultssupporting

confidence: 90%

“…Generally, the more disseminated a word is, the more likely it is to grow. This holds for social dissemination across users and threads (Altmann et al, 2011) as well as linguistic dissemination across different lexical collocations (Stewart and Eisenstein, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Concretely, we draw upon the Baumgartner Reddit Corpus, a collection of (almost) all publicly available comments posted on Reddit since 2005. 4 A three-year slice of this corpus was used in a study on lexical change by Stewart and Eisenstein (2018). Gaffney and Matias (2018) show that the corpus's coverage of Reddit is not complete, but we do not expect this to affect our analysis.…”

Section: Experimental Datamentioning

confidence: 99%

“…Usage frequency is a well-known factor that influences the evolution of a word at historical time scales (Pagel et al, 2007). Studies on lexical change in online groups have shown that this is also true for shorter time scales (Altmann et al, 2011;Stewart and Eisenstein, 2018). Another main factor is the dissemination of a word, i.e., how widely a word is spread across different social and linguistic contexts.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Predicting the Growth of Morphological Families from Social and Linguistic Factors

Hofmann¹,

Pierrehumbert²,

Schütze³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as "trump", "antitrumpism", and "detrumpify", in social media. We introduce the novel task of Morphological Family Expansion Prediction (MFEP) as predicting the increase in the size of a morphological family. We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark. Our experiments demonstrate very good performance on MFEP.

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Section: Resultssupporting

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Growth of Morphological Families from Social and Linguistic Factors

Hofmann¹,

Pierrehumbert²,

Schütze³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…Language change is a popular topic of research in linguistics (Stephen, 1962). In natural language processing, using data from online platforms such as Twitter or discussion fora, language change and adoption have been studied at the community level (Danescu-Niculescu-Mizil et al, 2013;Eisenstein et al, 2014;Goel et al, 2016;Stewart and Eisenstein, 2018) and at the individual level (Zhang et al, 2019). In some cases, the senses of the same word are known to shift over time (Wijaya and Yeniterzi, 2011), and modeling such changes in word semantics has been explored using diachronic word embeddings (Kulkarni et al, 2015;Hamilton et al, 2016;Kutuzov et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Temporally-Informed Analysis of Named Entity Recognition

Rijhwani¹,

Preoţiuc-Pietro²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Natural language processing models often have to make predictions on text data that evolves over time as a result of changes in language use or the information described in the text. However, evaluation results on existing data sets are seldom reported by taking the timestamp of the document into account. We analyze and propose methods that make better use of temporally-diverse training data, with a focus on the task of named entity recognition. To support these experiments, we introduce a novel data set of English tweets annotated with named entities. 1 We empirically demonstrate the effect of temporal drift on performance, and how the temporal information of documents can be used to obtain better models compared to those that disregard temporal information. Our analysis gives insights into why this information is useful, in the hope of informing potential avenues of improvement for named entity recognition as well as other NLP tasks under similar experimental setups.

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Evolving linguistic divergence on polarizing social media

Karjus,

Cuskley

2024

Humanit Soc Sci Commun

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Language change is influenced by many factors, but often starts from synchronic variation, where multiple linguistic patterns or forms coexist, or where different speech communities use language in increasingly different ways. Besides regional or economic reasons, communities may form and segregate based on political alignment. The latter, referred to as political polarization, is of growing societal concern across the world. Here we map and quantify linguistic divergence across the partisan left-right divide in the United States, using social media data. We develop a general methodology to delineate (social) media users by their political preference, based on which (potentially biased) news media accounts they do and do not follow on a given platform. Our data consists of 1.5M short posts by 10k users (about 20M words) from the social media platform Twitter (now “X”). Delineating this sample involved mining the platform for the lists of followers (n = 422M) of 72 large news media accounts. We quantify divergence in topics of conversation and word frequencies, messaging sentiment, and lexical semantics of words and emoji. We find signs of linguistic divergence across all these aspects, especially in topics and themes of conversation, in line with previous research. While US American English remains largely intelligible within its large speech community, our findings point at areas where miscommunication may eventually arise given ongoing polarization and therefore potential linguistic divergence. Our flexible methodology — combining data mining, lexicostatistics, machine learning, large language models and a systematic human annotation approach — is largely language and platform agnostic. In other words, while we focus here on US political divides and US English, the same approach is applicable to other countries, languages, and social media platforms.

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

Cited by 18 publications

References 40 publications

Predicting the Growth of Morphological Families from Social and Linguistic Factors

Predicting the Growth of Morphological Families from Social and Linguistic Factors

Temporally-Informed Analysis of Named Entity Recognition

Evolving linguistic divergence on polarizing social media

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