"This was never about a virus": Perceptions of Vaccination Hazards and Pandemic Risk in #Covid19NZ Tweets.

In this paper, we draw on qualitative methods from the medical humanities and quantitative approaches from corpus linguistics to assess the different mappings of pandemic risks by Twitter (X) users employing the #Covid19nz hashtag. We look specifically at their responses to government measures around vaccines between August and November 2021. Risk, we reveal, was a major discursive thread in tweets during this period, but within our tweets, it was the vaccine rather than the virus around which hazard perception and response were grouped. We find that the discursive stance of those opposed to the vaccine evoked entangled medical and political hazards, untrustworthy experts, obscure information, restrictions on sovereignty, threats to children, and uncertain future dangers, all of which positioned them within what Ulrich Beck termed the world risk society. We also found that these narratives of risk manifested in specific Twitter styles, which employed a consistently larger number of hashtags. The lack of conjunctions between the hashtags, we argue, encouraged a disordered reading of doubt and precaution, as the hashtags presented triggering phrases whose interconnections were hinted at rather than specified. By contrast, those who tweeted in support of government measures were rhetorically led by solutions rather than risks, with one exception: their perception of those who were vaccine opposed. We use scholarship on risk and precautionary logic to map out the contrasting positions in tweets addressing Aotearoa New Zealand's pandemic experience during the closing months of 2021.

Bubbles and lockdown in Aotearoa New Zealand: the language of self-isolation in #Covid19NZ tweets.

In March 2020, as cases of COVID-19 were found in Aotearoa New Zealand, the government moved to eliminate community transmission of the virus through self-isolation. During this month, as the population discussed if, when and how households would be asked to stay at home, terms such as lockdown-the state of (national) closure-and bubble-the household isolating together-became common parts of everyday conversation.In this article, we blend quantitative and qualitative research methodologies from corpus linguistics, literary studies and the medical humanities to compare the affective range of the terms lockdown and bubble as they were used in tweets containing the hashtag #Covid19NZ. Both lockdown and bubble are metaphors of containment that provided different ways of understanding and engaging with government stay-at-home measures by highlighting and minimising different aspects of the event. We found that while the strong, prison connotations of lockdown were reflected in discussions of the measure as a tough form of control exercised from above, the lighter associations of the term bubble led to the perception of this measure as more malleable and conducive to exertion of individual control. Yet, although the seemingly restrictive range of lockdown made it a useful term for the expression of negative affect, the term was actually more frequently used with neutral or unclear affect to share information. Conversely, while bubble tweets expressed more positive sentiment, humour and support towards government stay-at-home measures, this rendered the term surprisingly restrictive in its potential uses: its lightness makes it an effective way to limit the expression of antilockdown sentiment. As Kiwi Twitter users faced the uncertainty of the first COVID-19 lockdown, the pre-existing connotations of the metaphors used to frame stay-at-home measures also helped frame their own experiences of these measures.

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.