In a recent study, researchers from the Massachusetts Institute of Technology were forced to concede that members of online pandemic skeptic communities both valued the scientific method and utilized a more professional caliber of data analysis and visualization to support their cases than pandemic supporters.
The Jan. 20 study, Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online sought to analyze Twitter networks and Facebook groups who dissented against the pro-lockdown and pro-masking narratives that seek to emulate a Chinese Communist Party response to the outbreak of the pandemic in Wuhan that has been relentlessly disseminated by Big Tech, legacy media, and the majority of world governments.
While the paper and its thesis is often oblique, utilizes denigrating terminology and framing of thought towards skeptics, and is rife with woke leftist verbiage and narratives surrounding extraneous topics such as climate change and the Jan. 6 Capitol riots, it nonetheless is forced to concede pandemic dissenters are rational and value the scientific method.
Upon a full read of the report, it becomes clear that the purpose of the paper was not to examine whether pandemic dissenters were rational in their thinking, following the scientific method to validate their position, or if their position were in line with the truth.
Instead, it should be said the authors and the focus of their study suffered a certain consternation as to the question of why pandemic dissenters wouldn’t simply accept or tow the party line put out by large media, government, and science-themed institutions, and were somewhat at a loss for what to suggest to force compliance.
In one case, MIT’s fellows made this clear when citing a separate study that found “that calling for increased media literacy can often backfire: the instruction to ‘question more’ can lead to a weaponization of critical thinking and increased distrust of media and government institutions.”
“There is a temptation in studies of this nature to describe these groups as ‘anti-science,’ but this would make it completely impossible for us to meaningfully investigate this article’s central question: understanding what these groups mean when they say ‘science’,” say the authors.
“This study forces us to see that coronavirus skeptics champion science as a personal practice that prizes rationality and autonomy; for them, it is not a body of knowledge certified by an institution of experts. Calls for data or scientific literacy therefore risk recapitulating narratives that anti-mask views are the product of individual ignorance rather than coordinated information campaigns that rely heavily on networked participation.”
Skepticism gets a dirty label
Late in the Implications and Conclusion section, researchers show their hand, “As we have seen, people are not simply passive consumers of media: anti-mask users in particular were predisposed to digging through the scientific literature and highlighting the uncertainty in academic publications that media organizations elide. When these uncertainties did not surface within public-facing versions of these studies, people began to assume that there was a broader cover-up.”
They also relayed concern because dissenters rejected an exclusively academic venue for the practice of science and instead “Espouse a vision of science that is radically egalitarian and individualist.”
The study uses the term “anti-mask” 79 times and “anti-maskers” 26 times throughout a brief 18 page report. It waits until the fourth page under a section titled Note on Terminology before clarifying the nature of the denigrating variable they have defined for the subjects they examine, “Throughout this study, we use the term ‘anti-mask’ as a synecdoche for a broad spectrum of beliefs: that the pandemic is exaggerated, schools should be reopening, etc.”
The researchers claim that because online dissenters were found to sometimes use the word “maskers” to “describe people who are driven by fear” then the use of a polarizing and prejudicial term to describe the group was fair because “They are ‘anti-mask’ by juxtaposition.”
They also refer to dissenters as “users promulgating heterodox scientific positions about the pandemic (i.e., anti-maskers),” adding the communities “therefore act as a sounding board for thinking about how best to effectively mobilize the data towards more measured policies like slowly reopening schools.”
Methodology and findings
Researchers heavily used artificial intelligence to parse a vast swath of Facebook and Twitter posts to find the dataset they relied on in their study, which broke down six different sets of online communities:
- American Politics and Media – “American center-left, left, mainstream media, and popular or high profile figures (inside and outside of the scientific community)”;
- American Politics and Right Wing Media – “Includes members of the Trump administration, Congress, and right-wing personalities (e.g., @TuckerCarlson)”;
- British News Media – “Roughly corresponds to news media in the UK, with a significant proportion of engagement targeted at the Financial Times’ successful visualizations by reporter John Burn-Murdoch, as well as coverage of politician Nicola Sturgeon’s coronavirus policies”;
- Anti-Mask Network – “comprises over 2,500 users (9% of our network graph) and is anchored by former New York Times reporter @AlexBerenson, blogger @EthicalSkeptic, and @justin_hart”;
- New York Times-Centric Network – “This community is largely an artifact of a single visualization: Andy Slavitt (@ASlavitt), the former acting Administrator of the Centers for Medicare and Medicaid Services under the Obama administration, posted a viral tweet announcing the New York Times had sued the CDC”; and
- World Health Organization and Health-Related News Organizations – “This community consists of global health organizations, particularly the @WHO and its subsidiary accounts (e.g., @WHOSEARO for Southeast Asia). The user with the most followers in this community is @YouTube.”
The study was entirely focused on the fourth group, while the other five groups were used to create a smattering of data visualizations and at times provide a dataset to juxtapose against the Anti-Mask Network.
In its findings, reviews for lockdown skeptics were rather glowing when measured with the traditional principles of science. MIT’s researchers had to admit the community boasted “the highest percentage of verified users,” shared “the second-highest number of charts across the top six communities,” and were “the most prolific producers of area/line charts.”
They also found the group shared the fewest amount of photos, which is significant in that the study notes photos are mostly memes and images of politicians, which serve to drive public opinion, but are of little use in a data-driven scientific discussion.
The high percentage of verified users is also indicative that the group is dense with real people rather than bot and public relations shill accounts, “Twitter verification can often indicate that the account of public interest is authentic (subject to Twitter’s black-boxed evaluation standards); it can be a reasonable indication that the account is not a bot,” reads the study.
Researchers warned of the power these citizen-composed communities could generate through data analysis in driving public opinion away from the narrative espoused through authoritative sources, “Strategic information operations require the participation of online communities to consolidate and amplify these messages: these messages become powerful when emergent, organic crowds (rather than hired trolls and bots) iteratively contribute to a larger community with shared values and epistemologies.”
MIT’s team found pandemic dissenters formed a formidable online force in their own right, exhibiting “very similar patterns to the rest of the networks in our dataset.” The skeptic’s network also boasted the second-highest percentage of in-network retweets and the third-highest percentage of original tweets.
Dissenter communities have quality standards
Not only that, but they found dissenters were data-oriented rather than prejudicial in their approach to messaging, “These statistics suggest that anti-maskers tend to be among the most prolific sharers of data visualizations on Twitter.”
“Many of the visualizations shared by anti-mask Twitter users employ visual forms that are relatively similar to charts that one might encounter at a scientific conference.”
“The Twitter analysis establishes that anti-maskers are prolific producers and consumers of data visualizations, and that the graphs that they employ are similar to those found in orthodox narratives about the pandemic. Put differently, anti-maskers use ‘data-driven’ narratives to justify their heterodox beliefs,” reads the study.
They also noted dissenters emphasized “following the data” and some communities had strict moderation policies prohibiting the posting of non-user-created content, aiming to have discussion be “guided solely by the data.”
“In other words, anti-maskers value unmediated access to information and privilege personal research and direct reading over “expert” interpretations.” [Emphasis Inherent]
Researchers found top members of these communities encouraged newcomers to begin their own data analysis and visualization projects and provided guidance on how to adjust and reverse engineer graphs and charts disseminated by governments and media outlets.
“These users want to understand and analyze the information for themselves, free from biased, external intervention.”
Coronavirus skeptics not lacking in rationality
Members of the “anti-mask” circle were also found by MIT to be rational in the sense they could take their own data and conclusions with a grain of salt, “While users contend that their data visualizations objectively illustrate how the pandemic is no worse than the flu, they are similarly mindful to note that these analyses only represent partial perspectives that are subject to individual context and interpretation.”
An example cited by researchers is as follows, “‘I’ve never claimed to have no bias. Of course I am biased, I’m human,’ says one prolific producer of anti-mask data visualizations. ‘That’s why scientists use controls…to protect ourselves from our own biases. And this is one of the reasons why I disclose my biases to you. That way you can evaluate my conclusions in context. Hopefully, by staying close to the data, we keep the effect of bias to a minimum’.”
Not only is the group found to be rational, but researchers had to concede many of the “anti-maskers” were established scientists in their own right, “Paradoxically, these groups also seek ways to validate their findings through the scientific establishment. Many users prominently display their scientific credentials (e.g., referring to their doctoral degrees or prominent publications in venues like Nature) which uniquely qualify them as insiders who are most well-equipped to criticize the scientific community.”
Being scientific is ‘political radicalization’
Throughout the study, researchers display certain vitriol towards the use of the classical scientific method to debunk official narratives. In one excerpt they claim the communities use data analysis to “enculturate their users” and are promoting data literacy for “inculcating heterodox ideology.”
They actually go as far as to say “The transmission of data literacy, then, becomes a method of political radicalization.”
MIT’s researchers appear to be handcuffed by the data they discovered and are forced to laud, however laced with cynicism their phrasing may be, pandemic dissenters for their approach and motives, “While these groups highly value scientific expertise, they also see collective analysis of data as a way to bring communities together within a time of crisis, and being able to transparently and dispassionately analyze the data is crucial for democratic governance.”
“In fact, the explicit motivation for many of these followers is to find information so that they can make the best decisions for their families—and by extension, for the communities around them.”
One example researchers cited was a case in Texas, where “a coalition of mayors, school board members, and city council people” launched a third party, independent investigation into the state’s data and found a “backlog of unaudited tests was distorting the data.”
The researchers took a moment to connect this group with a notorious problem facing the installation of socialism and globalization, American nationalism; “Data literacy, for antimaskers [sic], exemplifies distinctly American ideals of intellectual selfreliance [sic], which historically takes the form of rejecting experts and other elites.”
They found dissenters were characterized by a “Particular emphasis on the usurpation of scientific knowledge by a paternalistic, condescending elite that expects intellectual subservience rather than critical thinking from the lay public,” and “They also assert the value of independence in a society that they believe promotes an overall de-skilling and dumbing-down of the population for the sake of more effective social control.”
“Moreover, this is a subculture shaped by mistrust of established authorities and orthodox scientific viewpoints. Its members value individual initiative and ingenuity, trusting scientific analysis only insofar as they can replicate it themselves by accessing and manipulating the data firsthand.”
The team showed they understood the problem with garnering compliance to authoritative narratives did not lie, however, in their targets’ ignorance, “Arguing that anti-maskers simply need more scientific literacy is to characterize their approach as uninformed and inexplicably extreme.”
“This study shows the opposite: users in these communities are deeply invested in forms of critique and knowledge production that they recognize as markers of scientific expertise.”
The writers also said protestors were “prolific and skilled purveyors of data visualizations,” and in their opinion, “For members of this social movement, counter-visualization and anti-masking are complementary aspects of resisting the tyranny of institutions that threaten to usurp individual liberties to think freely and act accordingly.”
Skeptics’ parallel with computer sciences
The study expounded on the parallels between the skeptic groups’ operational methodology and various best practices found in computer sciences and data analysis, “Anti-mask science has extended the traditional tools of data analysis by taking up the theoretical mantle of recent critical studies of visualization.”
“For example, one of the most popular visualizations within the Facebook groups we studied were unit visualizations, which are popular among anti-maskers and computer scientists for the same reasons: they provide more information, better match a reader’s mental model, and they allow users to interact with them in new and more interesting ways.”
The concessions continue, “Similarly, these groups’ impulse to mitigate bias and increase transparency (often by dropping the use of data they see as ‘biased’) echoes the organizing ethos of computer science research that seeks to develop ‘technological solutions regarding potential bias’ or ‘ground research on fairness, accountability, and transparency’.”
“In other words, these groups see themselves as engaging deeply within multiple aspects of the scientific process—interrogating the datasets, analysis, and conclusions—and still university researchers might dismiss them in leading journals as ‘scientifically illiterate’.”