Twitter constitutes a rich resource for investigating language contact phenomena. In this paper, we report findings from the analysis of a large-scale diachronic corpus of over one million tweets, containing loanwords from te reo Māori, the indigenous language spoken in New Zealand, into (primarily, New Zealand) English. Our analysis focuses on hashtags comprising mixed-language resources (which we term hybrid hashtags), bringing together descriptive linguistic tools (investigating length, word class, and semantic domains of the hashtags) and quantitative methods (Random Forests and regression analysis). Our work has implications for language change and the study of loanwords (we argue that hybrid hashtags can be linked to loanword entrenchment), and for the study of language on social media (we challenge proposals of hashtags as “words,” and show that hashtags have a dual discourse role: a micro-function within the immediate linguistic context in which they occur and a macro-function within the tweet as a whole).

Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike.

Networks are being used to model an increasingly diverse range of real-world phenomena. This paper introduces an exploratory approach to studying loanwords in relation to one another, using networks of co-occurrence. While traditional studies treat individual loanwords as discrete items, we show that insights can be gained by focusing on the various loanwords that co-occur within each text in a corpus, especially when leveraging the notion of a hypergraph. Our research involves a case-study of New Zealand English (NZE), which borrows Indigenous Māori words on a large scale. We use a topic-constrained corpus to show that: (i) Māori loanword types tend not to occur by themselves in a text; (ii) infrequent loanwords are nearly always accompanied by frequent loanwords; and (iii) it is not uncommon for texts to contain a mixture of listed and unlisted loanwords, suggesting that NZE is still riding a wave of borrowing importation from Māori.

This article contributes the first corpus-based study of possession in Māori, the indigenous language of Aotearoa New Zealand. Like most Polynesian languages, Māori has a dual possessive system involving a choice between the so-called A and O categories. While Māori grammars describe these categories in terms of the inherent semantic relationship between the possessum and possessor, there have been no large-scale corpus analyses demonstrating their use in natural contexts. Social media provide invaluable opportunities for such linguistic studies, capturing contemporary language use while alleviating the burden of gathering data through traditional means. We operationalise semantic distinctions to investigate possession in Māori-language tweets, focusing on the [possessum a/o possessor] construction (e.g., te tīmatanga o te wiki ‘the beginning of the week’). In our corpus comprising 2500 tweets produced by more than 200 individuals, we find that users leverage a wide array of noun types encompassing many different semantic relationships. We observe not only the expected predominance of the O category, but also a tendency for examples described by Māori grammars as A-marked to instead be O-marked (59%). Although the A category persists in the corpus, our findings suggest that language change could be underway. Our primary dataset can be explored interactively online.

David Trye, Vithya Yogarajan, Jemma König, Te Taka Keegan, David Bainbridge, Mark Apperley. Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022. 2022.