{"id":20092,"date":"2021-03-07T14:18:24","date_gmt":"2021-03-07T08:33:24","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=20092"},"modified":"2021-03-07T14:19:55","modified_gmt":"2021-03-07T08:34:55","slug":"when-more-covid-19-data-doesnt-equal-more-understanding","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/when-more-covid-19-data-doesnt-equal-more-understanding\/","title":{"rendered":"When more Covid-19 data doesn\u2019t equal more understanding"},"content":{"rendered":"\n

CAMBRIDGE, Mass. (MIT News Office<\/em>)–\u00a0Since the start of the Covid-19 pandemic, charts and graphs have helped communicate information about infection rates, deaths, and vaccinations. In some cases, such visualizations can encourage behaviors that reduce virus transmission, like wearing a mask. Indeed, the pandemic has been hailed as the\u00a0breakthrough moment<\/a>\u00a0for data visualization.<\/p>\n\n\n\n

But new findings suggest a more complex picture. A study from MIT shows how coronavirus skeptics have marshalled data visualizations online to argue against<\/em> public health orthodoxy about the benefits of mask mandates. Such \u201ccounter-visualizations\u201d are often quite sophisticated, using datasets from official sources and state-of-the-art visualization methods.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The researchers combed through hundreds of thousands of social media posts and found that coronavirus skeptics often deploy counter-visualizations alongside the same \u201cfollow-the-data\u201d rhetoric as public health experts, yet the skeptics argue for radically different policies. The researchers conclude that data visualizations aren\u2019t sufficient to convey the urgency of the Covid-19 pandemic, because even the clearest graphs can be interpreted through a variety of belief systems.  <\/p>\n\n\n\n

\u201cA lot of people think of metrics like infection rates as objective,\u201d says Crystal Lee. \u201cBut they\u2019re clearly not, based on how much debate there is on how to think about the pandemic. That\u2019s why we say data visualizations have become a battleground.\u201d<\/p>\n\n\n\n

The\u00a0research<\/a>\u00a0will be presented at the ACM Conference on Human Factors in Computing Systems in May. Lee is the study\u2019s lead author and a PhD student in MIT\u2019s History, Anthropology, Science, Technology, and Society (HASTS) program and MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL), as well as a fellow at Harvard University\u2019s Berkman Klein Center for Internet and Society. <\/p>\n\n\n\n

Co-authors include Graham Jones, a Margaret MacVicar Faculty Fellow in Anthropology; Arvind Satyanarayan, the NBX Career Development Assistant Professor in the Department of Electrical Engineering and Computer When more Covid-19 data doesn\u2019t equal more understanding<\/strong>Science and CSAIL; Tanya Yang, an MIT undergraduate; and Gabrielle Inchoco, a Wellesley College undergraduate.<\/p>\n\n\n\n

As data visualizations rose to prominence early in the pandemic, Lee and her colleagues set out to understand how they were being deployed throughout the social media universe. \u201cAn initial hypothesis was that if we had more data visualizations, from data collected in a systematic way, then people would be better informed,\u201d says Lee. To test that hypothesis, her team blended computational techniques with innovative ethnographic methods.<\/p>\n\n\n\n

They used their computational approach on Twitter, scraping nearly half a million tweets that referred to both \u201cCovid-19\u201d and \u201cdata.\u201d With those tweets, the researchers generated a network graph to find out \u201cwho\u2019s retweeting whom and who likes whom,\u201d says Lee. <\/p>\n\n\n\n

\u201cWe basically created a network of communities who are interacting with each other.\u201d Clusters included groups like the \u201cAmerican media community\u201d or \u201cantimaskers.\u201d The researchers found that antimask groups were creating and sharing data visualizations as much as, if not more than, other groups.<\/p>\n\n\n\n

And those visualizations weren\u2019t sloppy. \u201cThey are virtually indistinguishable from those shared by mainstream sources,\u201d says Satyanarayan. \u201cThey are often just as polished as graphs you would expect to encounter in data journalism or public health dashboards.\u201d<\/p>\n\n\n\n

\u201cIt\u2019s a very striking finding,\u201d says Lee. \u201cIt shows that characterizing antimask groups as data-illiterate or not engaging with the data, is empirically false.\u201d<\/p>\n\n\n\n

Lee says this computational approach gave them a broad view of Covid-19 data visualizations. \u201cWhat is really exciting about this quantitative work is that we\u2019re doing this analysis at a huge scale. There’s no way I could have read half a million tweets.\u201d<\/p>\n\n\n\n

But the Twitter analysis had a shortcoming. \u201cI think it misses a lot of the granularity of the conversations that people are having,\u201d says Lee. \u201cYou can\u2019t necessarily follow a single thread of conversation as it unfolds.\u201d For that, the researchers turned to a more traditional anthropology research method \u2014 with an internet-age twist.<\/p>\n\n\n\n

Lee\u2019s team followed and analyzed conversations about data visualizations in antimask Facebook groups \u2014 a practice they dubbed \u201cdeep lurking,\u201d an online version of the ethnographic technique called \u201cdeep hanging out.\u201d Lee says \u201cunderstanding a culture requires you to observe the day-to-day informal goings-on \u2014 not just the big formal events. Deep lurking is a way to transpose these traditional ethnography approaches to digital age.\u201d<\/p>\n\n\n\n

The qualitative findings from deep lurking appeared consistent with the quantitative Twitter findings. Antimaskers on Facebook weren\u2019t eschewing data. Rather, they discussed how different kinds of data were collected and why. \u201cTheir arguments are really quite nuanced,\u201d says Lee. \u201cIt\u2019s often a question of metrics.\u201d For example, antimask groups might argue that visualizations of infection numbers could be misleading, in<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cI’ve been to livestreams where people screen share and look at the data portal from the state of Georgia,\u201d says Lee. \u201cThen they\u2019ll talk about how to download the data and import it into Excel.\u201d<\/p>\n\n\n\n

Jones says the antimask groups\u2019 \u201cidea of science is not listening passively as experts at a place like MIT tell everyone else what to believe.\u201d He adds that this kind of behavior marks a new turn for an old cultural current. \u201cAntimaskers\u2019 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.\u201d<\/p>\n\n\n\n

He adds that \u201cmaking sense of these complex dynamics would have been impossible\u201d without Lee\u2019s \u201cvisionary leadership in masterminding an interdisciplinary collaboration that spanned SHASS and CSAIL.\u201d<\/p>\n\n\n\n

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, \u201cantimaskers see the pandemic differently, using data that is quite similar. I still think data analysis is important. But it\u2019s certainly not the salve that I thought it was in terms of convincing people who believe that the scientific establishment is not trustworthy.\u201d<\/p>\n\n\n\n

Lee says their findings point to \u201ca larger rift in how we think about science and expertise in the U.S.\u201d That same rift runs through issues like climate change and vaccination, where similar dynamics often play out in social media discussions.<\/p>\n\n\n\n

To make these results accessible to the public, Lee and her collaborator, CSAIL PhD student Jonathan Zong, led a team of seven MIT undergraduate researchers to develop an interactive narrative<\/a> where readers can explore the visualizations and conversations for themselves.<\/p>\n\n\n\n

Lee describes the team\u2019s research as a first step in making sense of the role of data and visualizations in these broader debates. \u201cData visualization is not objective. It\u2019s not absolute. It is in fact an incredibly social and political endeavor. We have to be attentive to how people interpret them outside of the scientific establishment.\u201d<\/p>\n\n\n\n

This research was funded, in part, by the National Science Foundation and the Social Science Research Council.<\/p>\n","protected":false},"excerpt":{"rendered":"

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