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

Topic modeling identified ten themes that are recorded in Figure 6a . Keywords are listed in order of weight in forming the abstract topics found within the text. A tweet may include multiple topics, but typically has one predominant topic. The most common predominant topic was the economic and political impact, followed by government response to the virus, then discussion of the outbreak and its development and transmission. The least common topics included index cases, the public health response, and healthcare provision. Other topics included the number of cases and death as well as prevention and large-scale quarantine. An interactive visualization of tweet themes showing their development by day is available at https://ssaleh2.github.io/Early_2019nCoV_Twitter_Analysis/; hovering over a node will show the tweet text and the day it was posted (please note the figure is slow to load and the slider on top allows navigation through time). Figure 6b shows three screenshots from the visualization. Major themes clustered in the center while more obscure tweets displayed in the periphery. As tweets may include multiple topics, there is visible cross-over between topic clusters in the visualization. Topic clusters that included themes of outbreak and its transmission, public health risk, and index cases were discussed from the start of the study period, while discussion of quarantine effects, economic and political impact, and government response increased significantly in the second week of the study period.