Critical Discourse Analysis of Trump's Tweets on the Covid-19 Pandemic
This paper presents a critical discourse analysis of Donald Trump's tweets regarding COVID-19, utilizing Fairclough's three-dimensional model and Halliday's systemic functional linguistics to examine the language used. The analysis centers on two tweets that illustrate Trump's p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Perkumpulan Alumni dan Santri Mahyajatul Qurro'
2024-01-01
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| Series: | Ascarya |
| Subjects: | |
| Online Access: | https://journal.ascarya.or.id/index.php/iscs/article/view/660 |
| Summary: | This paper presents a critical discourse analysis of Donald Trump's tweets regarding COVID-19, utilizing Fairclough's three-dimensional model and Halliday's systemic functional linguistics to examine the language used. The analysis centers on two tweets that illustrate Trump's politicization of the pandemic and the propagation of stigmatization through social media. The research examines the stylistic and formal characteristics of Trump's tweets, focusing on his use of metaphorical language and blame-shifting tactics, specifically his labeling of COVID-19 as a 'Chinese virus.' The analysis also investigates discourse practices, connecting Trump's rhetoric on the U.S.-China trade agreement and pandemic response to wider political narratives. The study analyzes the socio-cultural implications of Trump's discourse at a macro level. It highlights how his discourse exacerbated xenophobia, nationalism, and social divisions during the global health crisis. The findings demonstrate that Trump's tweets influenced public perceptions and international relations, reinforcing anti-China attitudes and contributing to a polarized public response to the pandemic. This study emphasizes the significance of a critical and reflective approach to political communication, particularly during times of crisis. It also offers insights into the creation and dissemination of ideological narratives in the digital era.
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| ISSN: | 2798-5083 2775-4243 |