International Journal of Corpus Linguistics - Volume 26, Issue 4, 2021

Covid-19, the greatest global health crisis for a century, brought a new immediacy and urgency to international bio-medical research. The pandemic generated intense competition to produce a vaccine and contain the virus, creating what the World Health Organization referred to as an ‘infodemic’ of published output. In this frantic atmosphere, researchers were keen to get their research noticed. In this paper, we explore whether this enthusiasm influenced the rhetorical presentation of research and encouraged scientists to “sell” their studies. Examining a corpus of the most highly cited SCI articles on the virus published in the first seven months of 2020, we explore authors’ use of hyperbolic and promotional language to boost aspects of their research. Our results show a significant increase in hype to stress certainty, contribution, novelty and potential, especially regarding research methods, outcomes and primacy. Our study sheds light on scientific persuasion at a time of intense social anxiety.

Research dissemination through academic blogs creates opportunities for writers to reach wider audiences. With COVID-19, public dissemination of research impacts daily practices, and national and international policies, and in countries like the UK and Spain, The Conversation publishes accessible COVID-19 themed research. Such academic blogs are important to the global academy, yet the role of authorial stance therein is notably under-investigated. This paper presents a corpus-based contrastive analysis of “stance nouns + that/de que” in a comparable corpus of English and Spanish COVID-19 themed academic blogs from The Conversation. The analysis identifies similarities and differences across languages that reflect how COVID-19 is framed in each language. For example, Spanish academics use Possibility and Factualness nouns when self-sourcing their stances with expanding strategies, while English academics use Argument and Idea nouns with external sources in contracting strategies. Overall, this paper adds to current linguistic knowledge on academic blogs and scientific communication surrounding COVID-19.

This paper presents an annotation approach to examine uncertainty in British and German newspaper articles on the coronavirus pandemic. We develop a tagset in an interdisciplinary team from corpus linguistics and sociology. After working out a gold standard on a pilot corpus, we apply the annotation to the entire corpus drawing on an “annotation-by-query” approach in CQPWeb, based on uncertainty constructions that have been extracted from the gold standard data. The annotated data are then evaluated and sociologically contextualised. On this basis, we study the development of uncertainty markers in the period under study and compare media discourses in Germany and the UK. Our findings reflect the different courses of the pandemic in Germany and the UK as well as the different political responses, media traditions and cultural concerns: While markers of fear are more important in British discourse, we see a steadily increasing level of disagreement in German discourse. Other forms of uncertainty such as ‘possibility’ or ‘probability’ are similarly frequent in both discourses.

This study analyzes attitudinal positioning in academic and media discourse pertaining to COVID-19 from the COVID-19 Corpus and Coronavirus Corpus, using a discourse dynamics approach. Underpinning this approach is the Complex Dynamic Systems Theory (CDST), which we employ to examine the discursive practices of a discourse event across time periods (timescales). The analysis identified significant differences in attitudinal markers and noteworthy developmental patterns in attitude positioning; the developmental trajectories of attitude construction were characterized by a nonlinear developmental pattern subject to fluctuations and variability. We also discerned the existence of dynamic interaction between the uses of attitudinal markers and the reported cases of COVID-19. Methodologically, we demonstrate how the integration of the discourse dynamics approach with corpus linguistics strengthens the social contextualization of data by enabling the identification of developmental patterns of targeted language features over time, and the interconnections of these language features with contextually important social factors.

This paper reports on study of online COVID-19 memorials posted during 2020 to the Church of England website https://www.rememberme2020.uk/. The paper employs a Corpus-Assisted Discourse Studies (CADS) approach to analyse networks of co-occurring linguistic items (types and lemmata) and patterns (ngrams) within online memorials, and examines how these frequent items/patterns exist within networked discourses that underpin an overarching bereavement discourse. The analysis finds that bereavement discourse is underpinned by frequent reference to love, relationships and relational identification, time and temporality, loss/absence, and memory, as well as metaphors based on container and journey image schema. An analysis of these metaphorical representations of death and bereavement suggest that online memorials serve as a space for the social practice of bereavement and shows how the language used to grieve attempts to ideologically (re)present the relationships between the bereaved and decedent. All code used in this paper can be found at https://osf.io/khcj2/.

This paper discusses the creation and use of the Coronavirus Corpus, which is currently (March 2021) 900 million words in size, and which will probably be about one billion words in size by May–June 2021. The Coronavirus Corpus is a subset of the NOW Corpus (News on the Web), which is currently about 12.1 billion words in size and which grows by about two billion words each year. These two corpora are updated every night, with about 6–10 million words for NOW and 2–3 million words for the Coronavirus Corpus. The Coronavirus Corpus allows users to see the frequency of words and phrases over time (even by individual day), and users can find all words that are more frequent in one time period than another. Users can also see the collocates for words and phrases, and compare the collocates to see what is being said about particular topics over time.