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_17
Snippet: A total of 126,049 tweets (of which, 123,407 were unique) from 53,196 unique users were collected during the study period. The most prevalent identification hashtag found was #coronavirus followed by #wuhancoronavirus present in 82% and 13% of tweets, respectively. The tweets accumulated 114,635 replies, 1,248,118 retweets, and 1,680,253 likes. In the first week of our analysis, the number of COVID-19-related tweets remained stable with less than.....
Document: A total of 126,049 tweets (of which, 123,407 were unique) from 53,196 unique users were collected during the study period. The most prevalent identification hashtag found was #coronavirus followed by #wuhancoronavirus present in 82% and 13% of tweets, respectively. The tweets accumulated 114,635 replies, 1,248,118 retweets, and 1,680,253 likes. In the first week of our analysis, the number of COVID-19-related tweets remained stable with less than 100 tweets per hour. The number of tweets per hour began increasing on January 20th, and reached as many as 250 per hour by January 21st and continued to grow with a peak of over 1,700 tweets per hour by January 28th, 2020. This trend closely tracked the number of newly confirmed COVID-19 cases ( Figure 1 ).
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