Internal Variation

Lee, Aldrin P.; Borlongan, Ariane Macalinga

doi:10.4324/9780429427824-13

Cited by 4 publications

(2 citation statements)

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“…Altogether, the findings relevant to geography indicate that internal variation in Twitter-style PhE is very much present, and that the variation is constrained in part by geography. The findings corroborate previous work, which also found patterns of internal variation in (Twitter-style) PhE being sensitive to geographical location (Villanueva 2016;Gonzales 2017;Lee & Borlongan 2022;Gonzales 2023b). The results related to age presentation show that those who present as young (or rather, those are classified as younger by the Deep Learning algorithm) had splitting rates that are lower than those presenting as older ( pd pre = 1, pd post = 0.99) (table 2 and figure 7c).…”

Section: Language-external Factorssupporting

confidence: 91%

“…the United States). It also hopes to contribute to our understanding of internal variation in Twitter-style PhE and PhE in general, which has often been thought to be negligible, if not non-existent (Llamzon 1997;Lee & Borlongan 2022). Finally, from a methodological perspective, the article also hopes to (i) normalize the inclusion of language-external variables in variationist analyses in the region; (ii) popularize the use of social media data for linguistic analyses; and (iii) encourage the use of state-of-the-art machine learning techniques (e.g.…”

Section: Introductionmentioning

confidence: 99%

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When to (not) split the infinitive: factors governing patterns of syntactic variation in Twitter-style Philippine English

GONZALES

2024

English Language and Linguistics

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The variability of adverbial placement in the modified infinitive construction (i.e. split infinitives vs. full infinitives with adverbial pre- and post-modification) has been widely discussed in the (American English) literature. Yet a convincing generalized explanation for the variation that simultaneously incorporates language-internal and language-external factors has yet to be found, particularly in English varieties that have not received as much scholarly attention as standardized varieties. This article investigates modified infinitive syntactic variation in Twitter-style Philippine English (PhE) using a 135-million-word Twitter corpus. It adopts a Bayesian approach in conducting a multiple multinomial regression analysis of the said variation, with the help of Deep-Learning-based demographic inference tools. Although the conditioning effects of some factors diverge from patterns discussed in prior work, the results generally show that language-internal (e.g. stress and rhythm, adverb type, adverb length) and language-external factors (i.e. time, age, sex, geography) jointly shape the choice to split the infinitive in this linguistic style of PhE.

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Section: Language-external Factorssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

When to (not) split the infinitive: factors governing patterns of syntactic variation in Twitter-style Philippine English

GONZALES

2024

English Language and Linguistics

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Broadening horizons in the diachronic and sociolinguistic study of Philippine English with the Twitter Corpus of Philippine Englishes (TCOPE)

Gonzales

2023

EWW

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This paper presents the Twitter Corpus of Philippine Englishes (TCOPE): a dataset of 27 million tweets amounting to 135 million words collected from 29 cities across the Philippines. It provides an overview of the dataset, and then shows how it can be employed to examine Philippine English (PhilE) and its relationship with extralinguistic factors (e.g. ethno-geographic region, time, sex). The focus is on the patterns of variation involving four PhilE features: (1) irregular past tense morpheme -t, (2) double comparatives, (3) subjunctive were, and (4) phrasal verb base from. My analyses corroborate previous work and further demonstrate structured heterogeneity within PhilE, indicating that it is a multifaceted and dynamic variety. TCOPE has shown itself to be useful for exploring both the “general” features of contemporary PhilE and the different forms of variation within it. It contributes to a deeper understanding of Philippine English(es) over time and in different social contexts.

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Sociolinguistic Analysis with Missing Metadata? Leveraging Linguistic and Semiotic Resources Through Deep Learning to Investigate English Variation and Change on Twitter

Gonzales

2024

Applied Linguistics

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This paper highlights a language and sign-based computational solution to the problem of missing social metadata on Twitter (now, ‘X’): demographic prediction using Deep Learning. It aims to apply this method to variationist sociolinguistics research, illustrating how the approach can facilitate analyses with missing metadata (i.e. stylistic age and sex/gender) by deriving this metadata solely from publicly available linguistic and semiotic resources on Twitter profiles (e.g. display pictures and biographies). I use my investigations of English tweets from the Philippines and Hong Kong as case examples, examining the extent to which the use of the copula and the use of will-shall modals on social media are conditioned by diachronic factors as well as factors internal and external to language (e.g. social factors). The results reveal the influence of stylistic gender and age as well as other factors on patterns of variation. They offer a glimpse into the nuanced sociolinguistic aspects of language usage on social media, highlighting the advantages of utilizing AI-powered Deep Learning to tackle data-related challenges. The discoveries and methodology hold the possibility of influencing other fields and practical situations beyond the study of language and society.

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Internal Variation

Cited by 4 publications

References 0 publications

When to (not) split the infinitive: factors governing patterns of syntactic variation in Twitter-style Philippine English

When to (not) split the infinitive: factors governing patterns of syntactic variation in Twitter-style Philippine English

Broadening horizons in the diachronic and sociolinguistic study of Philippine English with the Twitter Corpus of Philippine Englishes (TCOPE)

Sociolinguistic Analysis with Missing Metadata? Leveraging Linguistic and Semiotic Resources Through Deep Learning to Investigate English Variation and Change on Twitter

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