An ""Infodemic"": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak

Richard J. Medford; Sameh N. Saleh; Andrew Sumarsono; Trish M. Perl; Christoph U. Lehmann

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.03.20052936 doi: medRxiv preprint Figure 6a . The fifteen terms (in order of weighting) that contributed to each abstract topic with Figure 6b . A t-distributed Stochastic Neighbor Embedding (t-SNE) graph (which embeds highdimensional data into a two-dimensional space where similar tweets are grouped together) that visualizes the topics in Figure 6a as labeled by color and how they change over time. The full interactive visualization is available at https://ssaleh2.github.io/Early_2019nCoV_Twitter_Analysis/; please note the visualization is slow to load. Each node represents an individual tweet and only tweets posted through the day highlighted on the slider are shown in the foreground, while all tweets in the study period are shown in the background. Hovering over a node will show the tweet text and the day it was posted. Depicted here are three screenshots for January 14 (day 0), January 20 (day 6), and January 27 (day 13).