Geographically Weighted Regression Analysis for COVID-19 Twitter Data

At the time of writing, there were more than 148 million confirmed COVID-19 cases around the world, and the virus's spread has already wreaked havoc on the citizens, resources, and economies of many countries. Globally, social distancing steps such as travel bans, self-quarantines, and company closures are altering society's very structure. Since people are being forced out of public areas, much of the discussion on these issues now takes place on social networks such as Twitter. Communication platforms inspired by COVID-19 outbreak, and users exchange various messages to keep each other informed. In this regard, the relationship between COVID-19 data and Twitter messages from people in various countries was investigated in this paper. More than 66 million text tweets and 94 million location tweets were examined using the geographical weight regression method in the first four months of the COVID-19 outbreak to find correlation between corona data (including mortality, number of patients and recovered, and testers) and Twitter data (including users' tweets by post, geographical location, and photo). The results indicate that COVID-19 data had a significant impact on people's tweets in countries such as the United States, China, South Korea, and Japan. Furthermore, more than 61% of countries have a low standard deviation in detecting these spatial auto-correlations.

Geographically Weighted Regression Analysis for COVID-19 Twitter Data Keywords:

COVID-19 , spatial autocorrelation , GWR , Twitter