Wake up New Zealand! Directives, politeness and stance in Twitter #Covid19NZ posts.

As Covid-19 made its way to the shores of Aotearoa New Zealand in 2020, Kiwis took to Twitter to share their experiences and opinions regarding both the virus and government responses. In this paper, we examine a corpus of 1001 tweets to see just how Twitter users utilize different linguistic and politeness strategies when sharing directives conveying stance toward government Covid-19 measures. While there has been research into the use of directives in spoken and written language, there has been little exploration of how directives are used in the language of Social Media. Often considered difficult to classify even in more traditional language genres, Twitter corpora pose additional challenges. We propose a strategy to identify and classify directives using syntactic and pragmatic features, and use this strategy to identify linguistic patterns of both in relation to stance toward government Covid-19 measures. While we find that the most prototypical directive within our corpus is largely consistent with previous definitions, we also find that tweeters exhibit a striking amount of variation in directive strategy, emphasizing the need for a nuanced approach to directive identification and classification. We further note trends in the directive strategies utilized by tweeters expressing different stances toward government measures.

The history of number words in the world's languages-what have we learnt so far?

For over 100 years, researchers from various disciplines have been enthralled and occupied by the study of number words. This article discusses implications for the study of deep history and human evolution that arise from this body of work. Phylogenetic modelling shows that low-limit number words are preserved across thousands of years, a pattern consistently observed in several language families. Cross-linguistic frequencies of use and experimental studies also point to widespread homogeneity in the use of number words. Yet linguistic typology and field documentation reports caution against positing a privileged linguistic category for number words, showing a wealth of variation in how number words are encoded across the world. In contrast with low-limit numbers, the higher numbers are characterized by a rapid and morphologically consistent pattern of expansion, and behave like grammatical phrasal units, following language-internal rules. Taken together, the evidence suggests that numbers are at the cross-roads of language history. For languages that do have productive and consistent number systems, numerals one to five are among the most reliable available linguistic fossils of deep history, defying change yet still bearing the marks of the past, while higher numbers emerge as innovative tools looking to the future, derived using language-internal patterns and created to meet the needs of modern speakers. This article is part of the theme issue 'Reconstructing prehistoric languages'.

Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Maori and English.

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 Maori, 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).

Linguistic evidence supports date for Homeric epics.

The Homeric epics are among the greatest masterpieces of literature, but when they were produced is not known with certainty. Here we apply evolutionary-linguistic phylogenetic statistical methods to differences in Homeric, Modern Greek and ancient Hittite vocabulary items to estimate a date of approximately 710-760 BCE for these great works. Our analysis compared a common set of vocabulary items among the three pairs of languages, recording for each item whether the words in the two languages were cognate - derived from a shared ancestral word - or not. We then used a likelihood-based Markov chain Monte Carlo procedure to estimate the most probable times in years separating these languages given the percentage of words they shared, combined with knowledge of the rates at which different words change. Our date for the epics is in close agreement with historians' and classicists' beliefs derived from historical and archaeological sources.

How do we use language? Shared patterns in the frequency of word use across 17 world languages.

We present data from 17 languages on the frequency with which a common set of words is used in everyday language. The languages are drawn from six language families representing 65 per cent of the world's 7000 languages. Our data were collected from linguistic corpora that record frequencies of use for the 200 meanings in the widely used Swadesh fundamental vocabulary. Our interest is to assess evidence for shared patterns of language use around the world, and for the relationship of language use to rates of lexical replacement, defined as the replacement of a word by a new unrelated or non-cognate word. Frequencies of use for words in the Swadesh list range from just a few per million words of speech to 191 000 or more. The average inter-correlation among languages in the frequency of use across the 200 words is 0.73 (p < 0.0001). The first principal component of these data accounts for 70 per cent of the variance in frequency of use. Elsewhere, we have shown that frequently used words in the Indo-European languages tend to be more conserved, and that this relationship holds separately for different parts of speech. A regression model combining the principal factor loadings derived from the worldwide sample along with their part of speech predicts 46 per cent of the variance in the rates of lexical replacement in the Indo-European languages. This suggests that Indo-European lexical replacement rates might be broadly representative of worldwide rates of change. Evidence for this speculation comes from using the same factor loadings and part-of-speech categories to predict a word's position in a list of 110 words ranked from slowest to most rapidly evolving among 14 of the world's language families. This regression model accounts for 30 per cent of the variance. Our results point to a remarkable regularity in the way that human speakers use language, and hint that the words for a shared set of meanings have been slowly evolving and others more rapidly evolving throughout human history.