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_10
Snippet: Using a word cloud, we visualized the top three hundred words with larger font size representing greater frequency. We used a subset of keywords to identify tweets related to three common infection prevention and control (IPC) strategies, vaccination, and racial prejudice. Appendix Section A details the keywords used. We analyzed the incidence of these tweets over time and manually reviewed a random 10% subset to validate content, evaluate narrat.....
Document: Using a word cloud, we visualized the top three hundred words with larger font size representing greater frequency. We used a subset of keywords to identify tweets related to three common infection prevention and control (IPC) strategies, vaccination, and racial prejudice. Appendix Section A details the keywords used. We analyzed the incidence of these tweets over time and manually reviewed a random 10% subset to validate content, evaluate narratives present, and explore examples of misinformation.
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