Liu, Yufeng and Tay, Dennis (2023) Modelability of WAR metaphors across time in cross-national COVID-19 news translation:An insight into ideology manipulation. Lingua, 286. ISSN 0024-3841
Full text not available from this repository.Abstract
Previous studies have compared Covid metaphors across languages and national contexts, but seldom focus on the translation issue where news narratives of the same event may be different when translated for different readers. Another unexplored question is whether, and how, successive discursive observations across time in such narratives are related. To fill these gaps, this study employs the Box-Jenkins time series analysis (TSA) method to investigate whether and how WAR metaphor usage in Chinese-English COVID-19 news reports (source articles and their translations) can be fitted with ARIMA (Autoregressive Integrated Moving Average) models. These reports come from three different sources across the year 2020: the Chinese Global Times (GT), the American New York Times (NYT) and the British The Economist (TE). Results show that WAR metaphors in the source news of GT and TE are modelable with an autoregressive and moving average model. However, no models were found to fit their translation counterparts. By contrast, WAR metaphors in both NYT’s source and translated news were not modelable. These differences are further qualitatively analyzed with examples in context. The study may contribute to the existing debates on WAR frames in COVID-19 discourse by adding a translation and TSA angle.