Scientific paper analyzes anti-mask social media and decry that these people think for themselves!
Long thread analyzing an MIT paper that examines anti-mask groups on social media and comes to the conclusion that these anti-maskers believe science is a process (not an institution), are more sophisticated in scientific knowledge than their ideological adversaries, value unmediated access to info, privilege personal research over expert interpretation, value individual initiative and ingenuity, spot omisions and slanted data, approach the pandemic with more scientific rigor, and redpill/interpret the situation for others. Sounds like a win-win - the experts are recognizing the critical thinkers on QResearch, halfchan /pol/, and our frens!!!
+++++++++++++++++
https://twitter.com/commieleejones/status/1391754136031477760
https://archive.md/aVgwd
commie lee jones@commieleejones
MIT researchers 'infiltrated' a Covid skeptics community a few months ago and found that skeptics place a high premium on data analysis and empiricism.
"Most fundamentally, the groups we studied believe that science is a process, and not an institution."
https://arxiv.org/pdf/2101.07993.pdf
9:56 AM · May 10, 2021·Twitter Web App
"Indeed, anti-maskers often reveal themselves to be more sophisticated in their understanding of how scientific knowledge is socially constructed than their ideological adversaries, who espouse naïve realism about the “objective” truth of public health data."
"In other words, anti-maskers value unmediated access to information and privilege personal research and direct reading over “expert” interpretations."
"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."
"They are highly reflexive about the inherently biased nature of any analysis, and resent what they view as the arrogant self-righteousness of scientific elites."
"Many of the users believe that the most important metrics are missing from government-released data."
"One user wrote: 'Coding data is a big deal—and those definitions should be offered transparently by every state. Without a national guideline—we are left with this mess'."
"The lack of transparency within these data collection systems—which many of these users infer as a lack of honesty—erodes these users’ trust within both government institutions and the datasets they release."
"In fact, there are multiple threads every week where users debate how representative the data are of the population given the increased rate of testing across many states."
"These groups argue that the conflation of asymptomatic and symptomatic cases therefore makes it difficult for anyone to actually determine the severity of the pandemic."
"For these anti-mask users, their approach to the pandemic is grounded in more scientific rigor, not less."
"These individuals as a whole are extremely willing to help others who have trouble interpreting graphs with multiple forms of clarification: by helping people find the original sources so that they can replicate the analysis themselves, by referencing other reputable studies…
+++++++++++++++++
Full pdf - free access
https://arxiv.org/pdf/2101.07993.pdf
Several people tried archiving this on archive.md, but it only shows a grey screen.
+++++++++++++++++
The actual journal entry (free access):
https://arxiv.org/abs/2101.07993
DOI: 10.1145/3411764.3445211
[Submitted on 20 Jan 2021]
Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online
Crystal Lee, Tanya Yang, Gabrielle Inchoco, Graham M. Jones, Arvind Satyanarayan
Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government's pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.