Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Lee, Crystal et al. "Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online." Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, May 2021, Yokohama, Japan, Association for Computing Machinery, May 2021. © 2021 The Authors As Published http://dx.doi.org/10.1145/3411764.3445211 Publisher Association for Computing Machinery (ACM) Version Final published version Citable link https://hdl.handle.net/1721.1/131130 Terms of Use Creative Commons Attribution 4.0 International license Detailed Terms https://creativecommons.org/licenses/by/4.0/ Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online Crystal Lee Tanya Yang Gabrielle Inchoco [email protected] [email protected] [email protected] Massachusetts Institute of Technology Massachusetts Institute of Technology Wellesley College Cambridge, MA, USA Cambridge, MA, USA Wellesley, MA, USA Graham M. Jones Arvind Satyanarayan [email protected] [email protected] Massachusetts Institute of Technology Massachusetts Institute of Technology Cambridge, MA, USA Cambridge, MA, USA ABSTRACT 1 INTRODUCTION Controversial understandings of the coronavirus pandemic have Throughout the coronavirus pandemic, researchers have held up turned data visualizations into a battleground. Defying public health the crisis as a “breakthrough moment” for data visualization re- ofcials, coronavirus skeptics on US social media spent much of search [91]: John Burn-Murdoch’s line chart comparing infection 2020 creating data visualizations showing that the government’s rates across countries helped millions of people make sense of the pandemic response was excessive and that the crisis was over. This pandemic’s scale in the United States [44], and even top Trump ad- paper investigates how pandemic visualizations circulated on social ministration ofcials seemed to rely heavily on the Johns Hopkins media, and shows that people who mistrust the scientifc estab- University COVID data dashboard [70]. Almost every US state now lishment often deploy the same rhetorics of data-driven decision- hosts a data dashboard on their health department website to show making used by experts, but to advocate for radical policy changes. how the pandemic is unfolding. However, despite a preponderance Using a quantitative analysis of how visualizations spread on Twit- of evidence that masks are crucial to reducing viral transmission ter and an ethnographic approach to analyzing conversations about [25, 29, 105], protestors across the United States have argued for COVID data on Facebook, we document an epistemological gap local governments to overturn their mask mandates and begin re- that leads pro- and anti-mask groups to draw drastically diferent opening schools and businesses. A pandemic that afects a few, inferences from similar data. Ultimately, we argue that the deploy- they reason, should not impinge on the liberties of a majority to ment of COVID data visualizations refect a deeper sociopolitical go about life as usual. To support their arguments, these protestors rift regarding the place of science in public life. and activists have created thousands of their own visualizations, often using the same datasets as health ofcials. CCS CONCEPTS This paper investigates how these activist networks use rhetorics • Human-centered computing ! Empirical studies in visu- of scientifc rigor to oppose these public health measures. Far from alization; Visualization theory, concepts and paradigms; So- ignoring scientifc evidence to argue for individual freedom, anti- cial media; Ethnographic studies. maskers often engage deeply with public datasets and make what we call “counter-visualizations”—visualizations using orthodox KEYWORDS methods to make unorthodox arguments—to challenge mainstream narratives that the pandemic is urgent and ongoing. By asking digital ethnography, network analysis, Twitter, Facebook, data lit- community members to “follow the data,” these groups mobilize eracy, data visualization data visualizations to support signifcant local changes. We examine the circulation of COVID-related data visualiza- ACM Reference Format: Crystal Lee, Tanya Yang, Gabrielle Inchoco, Graham M. Jones, and Arvind tions through both quantitative and qualitative methods. First, we Satyanarayan. 2021. Viral Visualizations: How Coronavirus Skeptics Use conduct a quantitative analysis of close to half a million tweets Orthodox Data Practices to Promote Unorthodox Science Online. In CHI that use data visualizations to talk about the pandemic. We use Conference on Human Factors in Computing Systems (CHI ’21), May 8–13, network analysis to identify communities of users who retweet the 2021, Yokohama, Japan. ACM, New York, NY, USA, 18 pages. https://doi.org/ same content or otherwise engage with one another (e.g., maskers 10.1145/3411764.3445211 and anti-maskers). We process over 41,000 images through a com- puter vision model trained by Poco and Heer [83], and extract Permission to make digital or hard copies of part or all of this work for personal or feature embeddings to identify clusters and patterns in visualiza- classroom use is granted without fee provided that copies are not made or distributed tion designs. The academic visualization research community has Thisfor proft work or is commercial licensed under advantage a Creative and thatCommons copies bearAttribution this notice International and the full citation traditionally focused on mitigating chartjunk and creating more 4.0on theLicense. frst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). intuitive visualization tools for use by non-experts; better visu- CHI ’21, May 8–13, 2021, Yokohama, Japan alizations, researchers argue, would aid public understanding of © 2021 Copyright held by the owner/author(s). data-driven phenomena. However, we fnd that anti-mask groups ACM ISBN 978-1-4503-8096-6/21/05. https://doi.org/10.1145/3411764.3445211 on Twitter often create polished counter-visualizations that would CHI ’21, May 8–13, 2021, Yokohama, Japan Lee, Yang, Inchoco, Jones, and Satyanarayan not be out of place in scientifc papers, health department reports, public understanding of the data. This paper empirically shows and publications like the Financial Times. how COVID anti-mask groups use data visualizations to argue that Second, we supplement this quantitative work with a six month- the US government’s response (broadly construed) to the pandemic long observational study of anti-mask groups on Facebook. The is overblown, and that the crisis has long been over. period of this study, March to September 2020, was critical as it These fndings suggest that the ability for the scientifc commu- spanned the formation and consolidation of these groups at the pan- nity and public health departments to better convey the urgency demic’s start. Quantitative analysis gives us an overview of what of the US coronavirus pandemic may not be strengthened by in- online discourse about data and its visual representation looks like troducing more downloadable datasets, by producing “better visu- on Twitter both within and outside anti-mask communities. Quali- alizations” (e.g., graphics that are more intuitive or efcient), or tative analysis of anti-mask groups gives us an interactional view by educating people on how to better interpret them. This study of how these groups leverage the language of scientifc rigor—being shows that there is a fundamental epistemological confict between critical about data sources, explicitly stating analytical limitations maskers and anti-maskers, who use the same data but come to of specifc models, and more—in order to support ending public such diferent conclusions. As science and technology studies (STS) health restrictions despite the consensus of the scientifc establish- scholars have shown, data is not a neutral substrate that can be used ment. Our data analysis evolved as these communities did, and for good or for ill [14, 46, 84]. Indeed, anti-maskers often reveal our methods refect how these users reacted in real time to the themselves to be more sophisticated in their understanding of how kaleidoscopic nature of pandemic life. As of this writing, Facebook scientifc knowledge is socially constructed than their ideological has banned some of the groups we studied, who have since moved adversaries, who espouse naive realism about the “objective” truth to more unregulated platforms (Parler and MeWe). of public health data. Quantitative data is culturally and histori- While previous literature in visualization and science commu- cally situated; the manner in which it is collected, analyzed, and nication has emphasized the need for data and media literacy as interpreted refects a deeper narrative that is bolstered by the col- a way to combat misinformation [43, 47, 89], this study fnds that lective efervescence found within social media communities. Put anti-mask groups practice a form of data literacy in spades. Within diferently, there is no such thing as dispassionate or objective data this constituency, unorthodox viewpoints do not result from a def- analysis. Instead, there are stories: stories shaped by cultural log- ciency

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