[Woman TV anchor voice over] Nearly one hundred Martin County residents
making it known they do not support a
recently passed mask mandate in the county.
[Bearded man] But don't try to make me wear a mask.
The order requiring people to wear masks in business and in outside
spaces where social distancing is impossible.
[Crystal's best ASMR voice] Controversial understandings of the coronavirus pandemic have
turned data visualizations into a battleground.
Coronavirus skeptics and anti-mask protestors throughout the country have campaigned against
major public health measures, arguing that the pandemic that affects
a few should not impinge on the liberties of a majority to go about life
as usual. While many researchers in computer science and public health
seek better forms of data and scientific literacy in order to redress
the problem, what does a data-driven approach to understanding
the pandemic really mean? For some, this breakthrough moment
for data visualization explains the popularity of line charts comparing the
infection rates across countries, or COVID data dashboards that show
how the pandemic is unfolding in individual counties.
But do the data show that the pandemic is exaggerated and that the crisis is over?
Unlike the liberal media, numbers don't lie.
It's critical – you know what? That we look at these numbers with a clear lens
From 2009 to 2010, nearly 61 million Americans got swine flu
Beyond that fact, we still don't have the numbers we need to fully understand what's happening around us
[Trump] Let's look at some of these charts. [Swan] I'd love to.
What's compelling about the rhetoric here is the emphasis on *following the numbers,*
where people might use the same data from the CDC and WHO and
yet draw radically different conclusions.
How does this work in practice, and how did we get here?
To start answering this question, we combine tools in computational social science
and digital ethnography to get a sense for how information circulates.
First, we took a bird's eye view by studying half a million tweets
and 41,000 visualizations computationally. We then combine that
with digital ethnography, so that we can identify the phenomena in a more granular fashion.
But let's be concrete here. When it comes to the computational social science,
we first collected all tweets mentioning some combination of the words "COVID"
and "data," and if they included an image.
Then, we created a network of Twitter users and classified the communities using the Louvain method
Concurrently, we classified all of the images
that were associated with these tweets using a computer vision model to ask
what kinds of data visualizations do different groups use?
Do anti-mask groups tend to use 3D bar charts compared to the New York Times graphics section?
We also conducted a digital ethnography using an iterative process.
We collected posts where people talked about COVID-related data,
then posts where people did their own COVID-related data analysis,
then, their angry posts where people who hate masks
talk about science and data. And we ultimately asked:
What does "follow the data" mean for anti-mask communities?
As we followed these different communities, we began to slowly
discern different modes of interpreting data. In our ethnographic
study of anti-maskers, we show how their discussions exemplify
a fundamental epistemological rift about how knowledge about the
pandemic should be made, interpreted, and shared.
While previous literature in visualization and science communication has emphasized the need
for data and media literacy as a way to combat misinformation,
this study finds that anti-mask groups practice a form of data literacy in spades.
Indeed, these groups often use orthodox
scientific methods to make unorthodox arguments.
Data visualizations are not a neutral window onto an observer
independent reality; during a pandemic, they are an arena of political struggle.
In these discussions, we find that anti-maskers think carefully about the grammar of graphics
by decomposing visualizations into layered components
and you can explore these different dynamics in the interactive piece that accompanies this article.
Data is not a neutral substrate that can be used for good or ill.
Anti-maskers often reveal themselves to be more sophisticated
in their understanding of how scientific knowledge is socially constructed than their ideological adversaries.
Put differently, there is no such thing as dispassionate or objective data analysis.
Instead, there are stories: stories shaped by cultural logics,
and animated by personal experience.
This story is about how a public health crisis, refracted through seemingly objective numbers
and data visualizations, is part of a broader battleground about
scientific epistemology and democracy in modern American life.