The assumption that people cannot investigate for themselves, or make decisions that reflect their best interests, is prevalent among social scientists. Cass Sunstein’s book Nudge, for example, is based on the concept that people’s choices can be manipulated because they do not recognize what to do with conflicting information. Studies into mainstream media have suggested that individuals do not do the work necessary to retrieve pertinent memories when decision making (Shrum, 2002, p. 71), and they only rely on the most recently received stimuli when forming opinions or judgments. These are beliefs formed by the way social scientists regard humans in general. A bias driven by the idea that we are nothing more than stimulus-response organisms, and evolutionary instincts, opposed to free will, force our actions. Behavioral Psychologist B.F. Skinner (1971, p. 101), stated this viewpoint should guide any study of human behavior. The idea of free will allows for too much variance and prohibits any standardization of behavioral analysis. This view dominates science and is, at its core, is fundamentally flawed. People do think and investigate on their own, while also coming to sensible conclusions. Citizen inquiry into controversial Covid-19 data has shaken the scientific establishment to the point where they are freely admitting they must find new ways to present their message. Online studies into groups that question the mainstream narrative are showing they not only recognize how to do research but can use it to challenge the powers that be.
A recent paper entitled Viral Visualizations: How coronavirus skeptics use orthodox data practices to promote unorthodox science online highlights how effectively conservative groups questioning the Covid-19 narrative, follow and interpret the so-called science. Scholars from the prestigious MIT and Wellesley college discuss the methods in which “anti-maskers” could interpret the same data justifying the extreme measures combating the pandemic and productively make counter-arguments. While they take note detailing the competent methodologies in which these groups compiled and interpreted data, they also seem to harbor a resentment that anyone would challenge the scientific consensus. They take the view that science is settled, and we should just follow expert opinions.
For example, the writers acknowledge that anti-mask groups are “very prolific in their creation of counter-visualizations, but they leverage data and their visual representations to advocate for and enact policy changes on the city, county, and state levels” (Lee, Yang, Inchoco, Jones & Satayanarayan, 2021, p. 2). This quote suggests there is some concern that anti-mask groups are finding unsettling truths in the data which suggests lockdown policies, social distancing and mask-wearing are unnecessary. Anti-mask groups also tend to be “more sophisticated in their understanding of how scientific knowledge is socially constructed than their ideological adversaries, who espouse naive realism about the “objective” truth of public health data” (Lee et al. 2021, p. 2).
The writers view scientific inquiry as a means to an end, an unquestionable consensus that defines what we should believe. Anti-mask groups reject this and view the scientific method as “radically egalitarian and individualist” (Lee et al. 2021, p. 15). The writers criticize Covid-19 skeptics for their view that scientific rigor should “prize rationality and autonomy” (Lee et al. 2021, p. 15). In other words, people who can interpret their data and use it to challenge the status quo threaten them. The article points to issues like Covid-19 being labeled as the primary cause of death in many patients when in fact, it was revealed that comorbidities caused most fatalities. In September 2020, the CDC admitted that 94% of coronavirus deaths were the result of other underlying medical conditions. The writers refer to this as a sleight of hand used to manipulate anti-masker data interpretations. Another point of discontent made by anti-mask groups is the inaccuracy of the data. The PCR tests, for example, were running a high rate of false positives because they were running a cycle of 36. Shortly after President Biden was inaugurated, they dropped the cycle count to 28 to reflect a more accurate rate. An anti-mask investigative team in Texas discovered a backlog of unaudited cases which was contributing to the state’s high positive rate (Lee et al. 2021, p. 13). A similar thing happened in Oklahoma. Late last summer they started adding probable cases dating as far back as March 2020. They admitted this would inflate the case numbers. Shortly after this news was reported, case number skyrocketed. The writers of this paper are not arguing these points are made up or based on misinformation. It shocks them to their core people can come to these conclusions and are rejecting the mainstream narrative.
How did they discover that anti-mask groups are so effective in compiling and interpreting their data? They infiltrated social media groups and ran studies so they could better learn to frame the message. They refer to this as Digital Ethnography (Lee et al. 2021, p. 4) and readily admit to lurking in online communities to document their attitudes and online activities.
“Using “lurking,” a mode of participating by observing specific to digital platforms, we propose “deep lurking” as a way of systematically documenting the cultural practices of online communities. Our methods here rely on robust methodological literature in digital ethnography, and we employ a case study approach to analyze these Facebook groups. To that end, we followed five Facebook groups (each with a wide range of followers, 10K-300K) over the first six months of the coronavirus pandemic, and we collected posts throughout the platform that included terms for “coronavirus” and “visualization” with Facebook’s Crowd Tangle tool. In our deep lurking, we archived web pages and took field notes on the following: posts (regardless of whether or not they included “coronavirus” and “data”), subsequent comments, Facebook Live streams, and photos of in-person events. We collected and analyzed posts from these groups from their earliest date to September 2020.” (Lee et al. 2021, p. 4)
Social scientists routinely infiltrate online groups to assess attitudes, opinions, and beliefs to gauge how effectively their message gains the desired compliance. For instance, the science journal JMIR Public Health and Surveillance conducted a study called Use of Health Belief Model–Based Deep Learning Classifiers for COVID-19 Social Media Content to Examine Public Perceptions of Physical Distancing: Model Development and Case Study. The aim of this study was to infiltrate social media communities and use the Health Belief Model as a framework from which they could evaluate attitudes towards social distancing interventions. The Health Belief Model is a model of behavioral change designed to examine people’s reactions to perceived health threats. By carefully analyzing social media interactions in response to information put out by health authorities, those framing the messages meant to influence behavior could change the message, as necessary. This study may not have contributed to the article being discussed; however, it does provide an example of how social media groups were infiltrated to acquire data.
There is a long-held belief that people cannot think for themselves and the scientific community is becoming frustrated that this is proving to be untrue. The question then becomes, how do you reframe the message so that anti-mask groups appear less credible? You do what leftists always do, accuse them of racism. Lee et al. (2021, p. 15) admit that convincing people to adopt the desired restrictive measures pertaining to Covid-19 will take more than accurate data. It will require “a sustained engagement with the social world of visualizations and the people who make or interpret them.” In other words, they need to make counter-arguments to steer people away from the anti-mask groups and the effective ways they are interpreting the data. They do this by equating those questioning the Covid narrative to anti-government groups and Christian fundamentalists who are “threatened by evolutionary biology” (Lee et al. 2021, p. 14). They even make the subtle suggestion that it is the questioning of mainstream science, and government institutions, which leads persuadable people to commit acts like the so-called insurrection at the capital this past January (Lee et al. 2021, p. 15). Finally, the writers readily admit that it would be necessary to appeal to the culture of wokeness to draw people away from anti-masker data interpretations.
Like data feminists, anti-mask groups similarly identify problems of political power within datasets that are released (or otherwise withheld) by the US government. Indeed, they contend that the way COVID data is currently being collected is non-neutral, and they seek liberation from what they see as an increasingly authoritarian state that weaponizes science to exacerbate persistent and asymmetric power relations. This paper shows that more critical approaches to visualization are necessary and that the frameworks used by these researchers (e.g., critical race theory, gender analysis, and social studies of science) are crucial to disentangling how anti-mask groups mobilize visualizations politically to achieve powerful and often horrifying ends. (Lee et al. 2021, p. 3)
American’s are waking up, and the establishment that believes people are nothing more than programmable automatons doesn’t know what to do about it. Aside from engaging in the same old rhetoric, they count on to discredit their opponents, they sit helplessly as people prove themselves to be capable of challenging the status quo, using their own systems against them. This is the type of citizen engagement that is necessary to retain liberty and hold government accountable. Unfortunately, leftist tactics of discrediting the opposition are effective at silencing many. While there are people effectively challenging the narrative, the masses remain naively compliant and seem unwilling to take a stand in their own interest. Anti-mask groups interpreting the data must at some point, realize what the left has; it will take more than skilled data interpretation to shine the light on the truth. It will take “a sustained engagement with the social world of visualizations and the people who make or interpret them” (Lee et al. 2021, p. 15). We must find ways to overcome the labels thrown on us by the left and engage with those sitting on the sidelines while also, bringing social credibility to our movement.
Bondy, D. (2020, September 1) CDC: 94% of Covid-19 Deaths Had Underlying Medical Conditions. MSN.com
Lee, C., Yang, T., Inchoco, D.G., Jones, M. G. & Satyanarayan, A. (2021) Viral Visualizations: How Coronavirus skeptics use orthodox data practices to promote unorthodox science online. CHI’21: Proceedings on the 2021 Conference on Human Factors in Computing Systems.
Lenthang, M. (2020, August 30) Experts: US Covid-19 Positivity Rate Due to ‘Too Sensitive’ Tests. Daily Mail
Mills, R. (2020, September 4) How, When and Why Oklahoma Will Begin Adding Probable Covid-19 to its Daily Reporting. KRMG.com
Raamkumar, A., Tan, G, S. & Wee, L, H. (2020) Use of the Health Belief Model-based deep learning classifiers for Covid-19 social media content to examine public perceptions of physical distancing: Model development and case study. JMIR Public health and surveillance. 6(3)
Shrum, L.J. Media consumption and perceptions of social reality: effects and underlying processes. From Media Effects: Advances in Theory and Research (2002) Lawrence Erlbaum Associates, Mahwah, New Jersey. Media Effects: Advances in Theory and Research, Second Edition (ethernet.edu.et)
Skinner, B. F. Beyond Freedom and Dignity. (1971) Pelican Books, Middlesex England. BF-Skinner-Beyond-Freedom-&-Dignity-1971.pdf (selfdefinition.org)
Sunstein, R., C. & Thaler, H., R. Nudge: Improving Decisions about Health, Wealth and Happiness. (2008) Caravan Books, Yale University Press. Richard_H._Thaler_Cass_R._Sunstein_Nudge_Improv. (14).pdf
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