Mapping Twitter Sentiments Towards Covid
Value-by-alpha map of sentiments with twitter representativeness level
Download Compressed
ImageDownload Full-Size Image
This bivariate map displays Twitter-based sentiments towards COVID-19
pandemic as
well as their representativeness level. The dataset is based on 738,581
geo-tagged English Tweets in contiguous U.S. from March 14 to June 11,
collected
using 74 keywords relevant to COVID-19 pandemics. The sentiments of tweet
content are generated using VADER package. At county level, the average
sentiment of individual tweet user is calculated in a positive-negative
scale,
while the representativeness level is denoted by the proportion of twitter
user
in total population (from American Community Survey 2014-2018). Inspired by
the
Value-by-Alpha map (Roth et al., 2010), we use the alpha / brightness value
to
illustrate the representativeness level, based on which the red color
displays
the positive sentiment, and the blue displays negative.
Click to
learn more about value-by-alpha map.