A discourse dynamics exploration of attitudinal responses towards COVID-19 in academia and media

Abstract

Abstract 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.

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