Understanding Public Perceptions and Information Sharing Patterns about Long Covid: A Qualitative Analysis of Twitter Data
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Blanes, Chelsea Kayren Joy
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Abstract
Long Covid is a set of symptoms experienced by patients who previously acquired an acute SARS-CoV-2 infection. Interestingly, Long Covid is considered the first illness that has been identified and named by the patients experiencing these symptoms. Due to its novelty, there was a lack of guidance and knowledge regarding its severity and biological mechanisms. Consequently, this resulted in high levels of uncertainty among the public which largely shaped the types of conversations about Long Covid, especially on social media. With Long Covid continuing to be an emerging scientific topic coupled with the apparent lack of research regarding the public’s need for Long Covid-related information, it necessitated formative research on the most salient conversations and opinions surrounding the illness. The top 100 most retweeted tweets containing the key phrase “Long Covid” from June 2020 to June 2022 were collected using the Twitter Streaming Application Programming Interface (API). The analysis included tracking rates of Long Covid-related misinformation, using deductive analysis to recognize patterns of the ever-changing public perceptions about Long Covid, and identifying the most salient categories through content analysis. This study outlines the general patterns observed in how Twitter users discussed and communicated their opinions/knowledge about Long Covid. This study also found that information from ‘trusted’ and ‘credentialed’ healthcare influencers is treated as the objective truth, while information from political figures is considered biased. This is one of the first studies that examined how information about Long Covid was shared on Twitter during the height of the pandemic. However, more research is needed to determine effective ways to combat misinformation, especially among vulnerable populations.
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Thesis (Master's)--University of Washington, 2023
