Data Visualizations Used In Conflicting Opinions on the COVID-19 Pandemic

Since the COVID-19 pandemic begun, graphs and charts have aided in the communication of information about rates of infection, vaccinations and death. Some of these graphics have also encourages some acts that reduce the rate of transmission like wearing a mask. The pandemic has been referred to as the breakthrough moment for data visualization.

However, new findings from MIT shows how coronavirus critics have rejected online data visualizations and argued against public health orthodoxy about the merits of mask mandates. These ‘counter-visualizations’ are quite complex, making use of datasets originating from official sources and advanced visualization methods.

The researchers searched through thousands of social media posts and discovered that coronavirus skeptics use counter-visualizations coupled with the same ‘follow-the-data’ oratory as public health experts, yet they argue for almost completely different policies. The researchers reached the conclusion that data visualizations aren’t adequate to pass cross the message of how urgent the COVID-19 pandemic is, because even the simplest graphs can be interpreted differently by people with different belief systems.

‘A lot of people think of metrics like infection rates as objective’, says Crystal Lee. ‘But they’re clearly not, based on how much debate there is on how to think about the pandemic. That’s why we say data visualizations have become a battleground’.

This research will be presented at the ACM Conference on Human Factors in Computing System in May. Lee is the lead author of the study and a PhD student in MIT’s History, Anthropology, Science, Technology and Society (HASTS) program, as well as MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). She is also a Fellow at Harvard University’s Berkman Klein Center for Internet and Society.

As data visualizations became rampant early in the pandemic, Lee and her colleagues took on the task of understanding how they were being used across the social media universe. ‘An initial hypothesis was that if we had more data visualizations, from data collected in a systemic way, then people would be better informed’, says Lee. To test that hypothesis, Lee and her team mixed computational techniques with innovative ethnographic methods.

They practiced their computational approach on Twitter, acquiring almost half a million tweets referring to both ‘Covid-19’ and ‘data’. With those tweets in their possession, the researchers generated a network to discover ‘who’s retweeting whom and who likes whom’, says Lee. ‘We basically created a network of communities who are interacting with each other’. Clusters included groups like the ‘American media community’ or the ‘Antimaskers’. The researchers discovered that the antimask groups were making use of data visualizations more than any other group.

And those visualizations were well done. ‘They are virtually indistinguishable from those shared by mainstream sources’, says Satyanarayan. ‘They are often just as polished as graphs you would expect to encounter in data journalism or public health dashboards.’

‘It’s a very striking finding. It shows that characterizing antimask groups as data-illiterate or not engaging with the data is empirically false’, says Lee.

Lee says their computational approach gave them a wider view of COVID-19 data visualizations. ‘What is really exciting about this quantitative work is that we’re doing this analysis at a huge scale. There’s no way I could have read half a million tweets.’

However, the Twitter analysis had a weakness. ‘I think it misses a lot of granularity of the conversations that people are having’, says Lee. ‘You can’t necessarily follow a single thread of conversation as it unfolds’. To achieve that, the researchers used a more traditional anthropology research method, with an internet-age twist.

Lee and his team followed and examined conversations about data visualizations in Facebook antimask Facebook groups— a practice they referred to as ‘deep lurking’, an online version of the ethnographic technique called ‘deep hanging out’. According to Lee, ‘understanding a culture requires you to observe the day-to-day informal goings-on— not just the big formal events. Deep lurking is a way to transpose these traditional ethnography approaches to digital age.’

The qualitative findings from deep lurking are consistent with the quantitative Twitter findings. Antimaskers on Facebook were not shirking data. Instead, they were discussing the methods and reasons different kinds of data was collected. ‘Their arguments are really quite nuanced’, says Lee. ‘It’s often a question of metrics.’

‘I’ve been to livestreams where people screen share and look at the data portal from the state of Georgia’, says Lee, ‘Then they’ll talk about how to download the data and import it into Excel’.

Jones believes the antimasks groups’ ‘idea of science is not listening passively as experts at a place like MIT tell everyone else what to believe’. He stated that this sort of behaviour is the beginning of a new age for an old cultural current. ‘Antimaskers’ 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 her online activities push those values into new arenas of public life’.

He adds that ‘making sense of these complex dynamics would have been impossible’ without Lee’s ‘visionary leadership in masterminding an interdisciplinary collaboration that spanned SHASS and CSAIL’.

According to Jevin West, Data Scientist at the University of Washington, the mixed methods research ‘advances our understanding of data visualizations in shaping public perception of science and politics’. Data visualizations ‘carry a veneer of objectivity and scientific precision. But as this paper shows, data visualizations can be used effectively on opposite sides of an issue’, he says. ‘It underscores the complexity of the problem— that it is not enough to “just teach media literacy”. It requires a more nuanced sociopolitical understanding of those creating and interpreting data graphics’.

Blending computational and anthropological insights led the researchers to a higher level of understanding of data literacy. Lee says their study revealed that when compared to public health orthodoxy, ‘antimaskers see the pandemic differently, using data that is quite similar. I still think data analysis is important. But it’s certainly not the salve that I thought it was in terms of convincing people who believe that the scientific establishment is not trustworthy’. Lee says their findings point to ‘a larger rift in how we think about science and expertise in the U.S.’. This rift is in issues like climate change and vaccinations whose discussions are quite similar on social media.

For 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 where readers can experience the visualizations and conversations for themselves.

Lee described the research of the team as the first step in understanding of the role of data and visualizations in debates like these. ‘Data visualization is not objective. It’s not absolute. It is in fact an incredibly social and political endeavor. We have to attentive to how people interpret them outside of the scientific establishment’.

Some of the 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 Science and CSAIL; Tanya Yang, an MIT undergraduate; and Gabrielle Inchoco, a Wellesley College undergraduate.

This research partly funded by the National Science Foundation and the Social Science Research Council.

By Marvellous Iwendi.

Source: MIT News