Since the start of the COVID-19 pandemic, charts and graphs have helped provide information on infection rates, deaths, and vaccinations. In some cases, this visualization may encourage behaviors that reduce the spread of the virus, such as wearing a mask. The pandemic has been seen as a breakthrough moment in data visualization.
But the new findings reveal a more complicated situation. A study by the Massachusetts Institute of Technology (MIT) has revealed how coronavirus skeptics use data visualizations online to protest the benefits of public health conservative mask rules. This kind of “anti-visualization” is usually quite complex, using official data sets and the most advanced visualization methods.
Researchers scoured hundreds of thousands of social media posts and found that COVID-19 skeptics often deploy anti-visualizations using “data-following” statements similar to those of public health experts. However, the policies advocated by skeptics are not entirely different. The researchers concluded that data visualization is not sufficient to convey the urgency of the COVID-19 pandemic, as even the most obvious diagrams can be explained by different belief systems.
“Many people think that indicators like infection rates are objective,” said Crystal Lee, “but they clearly are not, based on how big the debate is about how to think about the pandemic. That’s why we say data visualization has become a battlefield.”
The research will be presented at the ACM Computer Systems Human Factors Conference in May. Lee is the first author of this study and a PhD student at the MIT History, Anthropology, Science, Technology, and Society (HASTS) project and the MIT Computer Science and Artificial Intelligence Laboratory (CSEL). He is also a Harvard Berkman-researcher at the Klein Internet and Society Center. Co-authors give birth to Margaret-McVega College researcher Graham Jones and CSAIL NBX career development assistant professor Arvind Satyanarayana, MIT graduate Tanya Yang and Wellesley College graduate Gabrielle Inchoco in the Department of Anthropology, Electrical Engineering and Computer Science at ZE .
With the rise of data visualizations in the early stages of this pandemic, Lee and his colleagues began to understand how they were deployed in the social media arena. “An initial assumption is that if we have more data visualizations and collect data in a systematic way, people will be better informed,” Lee explained. To test this hypothesis, his team combined computing technology with innovative ethnographic methods.
He used his calculation method to collect nearly 500,000 tweets mentioning “COVID-19” and “data” on Twitter. From these tweets, the researchers created a network map to track who was retweeting whom and who liked whom. Lee said: “We have basically built a community network for mutual exchanges.” Groups include groups such as the “American Media Community” or “Anti-Mask”. Researchers have found that there are as many anti-mask groups as, or even more, than other groups in creating and sharing data visualizations.
However, these visualizations are not sloppy. Satyanarayana explained: “They are almost identical to the data shared by mainstream sources. They are often as complex as the charts you would expect to find in data news or public health dashboards.”
“This is a very compelling finding. It shows that it is empirically wrong to portray the anti-mask group as data illiterate or non-participating in the data,” Lee said. Apart from this, he also said that this calculation method has given him a comprehensive understanding of visualization of COVID-19 data. “The really exciting thing about this quantitative work is that we do this analysis on a large scale. It’s impossible for me to read 500,000 tweets.”
But Twitter’s analysis has a drawback. “I think it misses a lot of the nuances of people’s ongoing conversations. You don’t necessarily have to follow the line of conversation,” Lee said. For this reason, researchers turned to a more traditional anthropological research method—one with the distortion of the Internet age.
Lee’s team tracked and analyzed conversations about data visualization in the Anti-Mask Facebook group – they called this approach “deep latency.” “Understanding a culture requires you to observe everyday informal activities – not just large formal events. Deep latency is one way to move these traditional ethnographic methods into the digital age,” Lee said.
Deep Latency’s qualitative findings appear to be in line with Twitter’s quantitative findings. The anti-submarine agents at Facebook don’t shy away from data. Instead, they discussed how and why to collect different types of data. Lee said: “Their reasoning is really subtle. It is often a question of measurement standards. For example, the anti-mask team may argue that visualization of infection numbers may be misleading, partly due to measurement parameters such as deaths.” In contrast, the range of uncertainty in infection rates is wide. In response, team members often create their own anti-visualizations, and even guide each other in data visualization techniques. Huh.”
Jones pointed out that the scientific philosophy of anti-mask groups is not to passively listen to experts in places like MIT who are telling others what to believe. They believe this behavior is a new twist in an ancient cultural trend. “The use of data literacy by anti-masks reflects deep values of self-reliance and anti-experts in the United States, which can be traced back to the beginnings of the founding of the nation, but their online activities bring these values to new areas of public life. pushed.”
Furthermore, he added: “Without Lee’s visionary leadership, planning an interdisciplinary collaboration that extends to SHASS and CSAIL would be impossible to make sense of for these complex dynamics.”
Javin West, a data scientist at the University of Washington, said this mixed-method study advanced his understanding of the role of data visualization in shaping public perceptions of science and politics. West was not involved in this research. West said: “Data visualization has the cloak of objectivity and scientific accuracy. But as this article shows, data visualization can be used effectively as opposed to a problem. It emphasizes the complexity of the problem.” – ‘Teaching just media literacy’ is not enough. It requires a more detailed socio-political understanding of data charters and interpreters.”
By combining insights from computing and anthropology, researchers have a more detailed understanding of data literacy. Lee said his research shows that masked people look at epidemics in a different way than public health conservatism, but the data used are quite similar. Lee said their findings “are a huge gap in the way we think about science and expertise, in the United States.” This rift is also driven by issues such as climate change and vaccination, and similar dynamics are often reflected in social media discussions.
To make these results understandable to the public, Lee and his colleague—CSEL PhD student Jonathan Zong—led a team of 7 MIT graduate researchers. He developed an interactive narrative so readers could explore the visualization and dialogue on their own.
Lee called the team’s research the first step in understanding the role of data and visualization in these broader debates. “Data visualization is not objective. It is not perfect. In fact, it is an incredible social and political effort. We have to pay attention to how people interpret them outside of scientific institutions,” Lee said.
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