Shifting Narratives: A Corpus-based Discourse Analysis of American Media’s Portrayal of China’s COVID-19 Response

Zhiyu Liu, Kesumawati Abu Bakar

Abstract


Given the global interconnectedness, it becomes essential to critically assess how global events are portrayed in the media, as this could influence public understanding, stoke sentiments, and potentially impact international relations. During the COVID-19 pandemic, the portrayal of China’s response by the American media became a topic of interest and contention. However, A noticeable gap remains in understanding this portrayal across different pandemic stages. This article examines how American mainstream news media has characterised China’s efforts to combat the COVID-19 pandemic over time through a corpus-based discourse analysis. The study employs the analytical methods of n-gram, keyword, and concordance lines in corpus linguistics to investigate the frequency and salience of discourse surrounding COVID-19 in China within American media. The results indicate shifting narratives in portraying China’s pandemic response over time, with a noticeable trend towards negative representations. The negative representations are characterised by three themes: the stigmatisation of China, the undermining of China’s achievements in combating the pandemic, and the politicisation of the pandemic. The article also indicates the perceived challenges in collaboration between America, China, and the WHO in addressing the pandemic, leading to disagreements and conflicts and impacting global mitigation efforts. This study highlights the critical role media plays in shaping public perceptions and international cooperation during global crises. Additionally, it is important for media outlets, policymakers, and stakeholders to reflect thoughtfully on their narratives, avoiding stereotypical beliefs while taking into account their effects on international relations and global collaboration.

 

Keywords: COVID-19 in China; American media; narratives; representation; corpus-based discourse analysis


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References


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DOI: http://dx.doi.org/10.17576/3L-2024-3001-10

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