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_19
Snippet: Collected tweets contained 2,877,816 words and 15,955,720 characters. The most common word in our analysis was 'outbreak', numbering 11,549 times ( Figure 2 ). The other top fifteen most commonly used words and their frequency in descending order were: 'spread' (11, 290) , 'health' (9, 734) , 'confirm' (6, 897) , 'death' (5,819), 'city' (5,662), 'report' (5,662), 'first' (5, 431) , 'world' (5, 244) , 'travel' (5,049), 'hospital' (4, 405) , 'infec.....
Document: Collected tweets contained 2,877,816 words and 15,955,720 characters. The most common word in our analysis was 'outbreak', numbering 11,549 times ( Figure 2 ). The other top fifteen most commonly used words and their frequency in descending order were: 'spread' (11, 290) , 'health' (9, 734) , 'confirm' (6, 897) , 'death' (5,819), 'city' (5,662), 'report' (5,662), 'first' (5, 431) , 'world' (5, 244) , 'travel' (5,049), 'hospital' (4, 405) , 'infect' (4,388), 'SARS' (4, 133) , 'mask' (3, 996) , 'patient' (3, 981) , and 'country' (3, 885) .
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