Abstract
The recent emergence and spread of COVID-19 have altered the way the world operates. As this pandemic continues to run its course, both language educators and learners around the world are facing a unique set of challenges. In this day and age, there are no more relevant, pressing, or internationally ubiquitous news stories than those related to COVID-19. For L2 learners to have a seat at the global table, it is necessary to learn languages using news stories. Hence, the current study applied text mining techniques to explore and identify patterns among news stories related to COVID-19. In the study, a corpus collecting online news reports about COVID-19 was analyzed. A number of R packages including readtext, tidytext, ggplot2, and ggraph were jointly employed to extract key phrases and construct a graphic model underlying the news corpus. A popular term-extraction method often used in text mining—term frequency–inverse document frequency (TF-IDF)—was utilized to extract the key phrases from the news reports on the COVID-19 virus. A wordnet structure was then established to uncover potentially salient thematic components. The pedagogical implications for language education and vocabulary assessment are further discussed.
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