Title

Spatial Analysis of Disinformation in COVID-19 Related Tweets

Document Type

Article

Publication Date

9-10-2020

Keywords

GIS, discourse analysis, COVID-19, Coronavirus, spatial analysis, disinformation, social media activity, downplay

Abstract

COVID-19 has emerged as a global pandemic caused by its highly transmissible nature during the incubation period. In the absence of vaccination, containment is seen as the best strategy to stop virus diffusion. However, public awareness has been adversely affected by discourses in social media that have downplayed the severity of the virus and disseminated false information. This article investigates COVID-19 related Twitter activity in May 2020 to examine the origin and nature of disinformation and its relationship with the COVID-19 incidence rate at the state and county level. A geodatabase of all geotagged COVID-19 related tweets was compiled. Multiscale Geographically Weighted Regression was employed to examine the association between social media activity, population, and the spatial variability of disease incidence. Findings suggest that MGWR could explain 96.7% of the variations, and content analysis indicates a strong spatial relationship between social media activity and known cases of Covid-19. Discourse analysis was conducted on tweets to index tweets downplaying the pandemic or disseminating disinformation. Findings suggest that states where twitter users spread more disinformation and showed more resistance to pandemic management measures in May, have experienced a surge in the number of cases in July.

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