Author: Richard J. Medford; Sameh N. Saleh; Andrew Sumarsono; Trish M. Perl; Christoph U. Lehmann
Title: An ""Infodemic"": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak Document date: 2020_4_7
ID: a6p6ka8w_46
Snippet: We show that the frequency of tweets was associated with the number of infected individuals for the early stages of the COVID-19 pandemic. Tweets predominantly showed negative sentiment and were linked to emotions of fear primarily, as well as surprise and anger. While tweets with misinformation and societal prejudice were present, tweets were also significantly used to disseminate valuable public health information. Twitter offers novel opportun.....
Document: We show that the frequency of tweets was associated with the number of infected individuals for the early stages of the COVID-19 pandemic. Tweets predominantly showed negative sentiment and were linked to emotions of fear primarily, as well as surprise and anger. While tweets with misinformation and societal prejudice were present, tweets were also significantly used to disseminate valuable public health information. Twitter offers novel opportunities to public health and governmental agencies to not only track public perception of infectious outbreaks, but also to target messages of a public health nature based on user interest and emotion.
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