By Pennel Bird
The current establishment view and the conventional wisdom is that folks resisting the jab must be low-IQ dolts. But is that true?
In the midst of this desperately bigoted attempt to paint half of America with a broad brush of ignorance comes an MIT study about these inconveniently vaccine-hesitant citizens. One journalist recently asserted:
“Proponents of the vaccine are unwilling or unable to understand the thinking of vaccine skeptics — or even admit that skeptics may be thinking at all. Their attempts to answer skepticism or understand it end up poisoned by condescension, and end up reinforcing it.”
The writer goes on to aver that, in contravention to the media and the administration’s “anti-vaxxer” smears, the MIT study concluded that the methods used by those who argue against the efficacy of masks and the COVID vaccines are reasoned and intelligent. This “public health skepticism” he asserts, is “highly informed, scientifically literate and sophisticated in the use of data.” The MIT study further concluded that despite labeling these Americans “anti-vaxxers” and “anti-maskers,” and accusing them of conspiracy theorizing, the truth is far more nuanced. The venerable institution asserts the overwhelming calculus among these free-thinking Americans is based on a sophisticated cost-benefit analysis.
So much for besmirching nearly half of The United States as “ignorant.”
The researchers found that antimask groups were creating and sharing data visualizations as much as, if not more than, other groups.
And those visualizations weren’t sloppy. “They are virtually indistinguishable from those shared by mainstream sources,” says Satyanarayan. “They are often just as polished as graphs you would expect to encounter in data journalism or public health dashboards.”
“It’s a very striking finding,” says Lee. “It shows that characterizing antimask groups as data-illiterate or not engaging with the data, is empirically false.”
Lee says this computational approach gave them a broad view of Covid-19 data visualizations. “What is really exciting about this quantitative work is that we’re doing this analysis at a huge scale. There's no way I could have read half a million tweets.”
But the Twitter analysis had a shortcoming. “I think it misses a lot of the granularity of the conversations that people are having,” says Lee. “You can’t necessarily follow a single thread of conversation as it unfolds.” For that, the researchers turned to a more traditional anthropology research method — with an internet-age twist.
Lee’s team followed and analyzed conversations about data visualizations in antimask Facebook groups — a practice they dubbed “deep lurking,” an online version of the ethnographic technique called “deep hanging out.” Lee says “understanding a culture requires you to observe the day-to-day informal goings-on — not just the big formal events. Deep lurking is a way to transpose these traditional ethnography approaches to digital age.”
The qualitative findings from deep lurking appeared consistent with the quantitative Twitter findings. Antimaskers on Facebook weren’t eschewing data. Rather, they discussed how different kinds of data were collected and why. “Their arguments are really quite nuanced,” says Lee. “It’s often a question of metrics.” For example, antimask groups might argue that visualizations of infection numbers could be misleading, in part because of the wide range of uncertainty in infection rates, compared to measurements like the number of deaths. In response, members of the group would often create their own counter-visualizations, even instructing each other in data visualization techniques.
“I've been to livestreams where people screen share and look at the data portal from the state of Georgia,” says Lee. “Then they’ll talk about how to download the data and import it into Excel.”
Jones says the antimask groups’ “idea of science is not listening passively as experts at a place like MIT tell everyone else what to believe.” He adds that this kind of behavior marks a new turn for an old cultural current. “Antimaskers’ use of data literacy reflects deep-seated American values of self-reliance and anti-expertise that date back to the founding of the country, but their online activities push those values into new arenas of public life.”
He adds that “making sense of these complex dynamics would have been impossible” without Lee’s “visionary leadership in masterminding an interdisciplinary collaboration that spanned SHASS and CSAIL.”
The mixed methods research “advances our understanding of data visualizations in shaping public perception of science and politics,” says Jevin West, a data scientist at the University of Washington, who was not involved with the research. Data visualizations “carry a veneer of objectivity and scientific precision. But as this paper shows, data visualizations can be used effectively on opposite sides of an issue,” he says. “It underscores the complexity of the problem — that it is not enough to ‘just teach media literacy.’ It requires a more nuanced sociopolitical understanding of those creating and interpreting data graphics.”
Combining computational and anthropological insights led the researchers to a more nuanced understanding of data literacy. Lee says their study reveals that, compared to public health orthodoxy, “antimaskers see the pandemic differently, using data that is quite similar. I still think data analysis is important. But it’s certainly not the salve that I thought it was in terms of convincing people who believe that the scientific establishment is not trustworthy.” Lee says their findings point to “a larger rift in how we think about science and expertise in the U.S.” That same rift runs through issues like climate change and vaccination, where similar dynamics often play out in social media discussions.