The purpose of this study is to examine Donald Trump's political communication regarding COVID-19 on Twitter during the time period 01-01-2020 to 30-09-2020. This study is motivated by the importance of analyzing Trump's power of definition regarding the situation surrounding the national crisis caused by COVID-19. The research questions include aspects of identifying frequent problem definitions, who is responsible for various crisis and whether Trump is motivating it, all provided through Trump’s tweets during the time period of the study. The theoretical framework is constructed based on Entman (1993) as well as Matthes and Kohring (2008) to fulfill the purpose of the study and answer the research questions. The method is based on quantitative content analysis with qualitative elements. The method provided the ability to focus on the most frequent themes and topics. The analytical categories that have been used are: problem identification, problem definition, treatment recommendation, distribution of responsibility and whether it is motivated or not. Findings of the most frequent societal crises were: Health crisis, Invisible Enemy-crisis and Information-crisis. Based on these three frames were identified: China is responsible for causing a Health crisis in the USA, China is responsible for causing the Invisible enemy-crisis in the USA and The spreading of disinformation by certain actors is harmful for the USA. With support of previous research of Donald Trump's political communication and usage of Twitter, this study highlights the importance of critically analyzing Trump, belonging to the political elite, how he uses his power to define COVID-19.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-433065 |
| Date | January 2021 |
| Creators | Bergstedt, Sophie, Bäcklin Neijnes, Cajsalisa |
| Publisher | Uppsala universitet, Institutionen för informatik och media, Uppsala universitet, Institutionen för informatik och media |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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