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  1. Learning from lines: Critical COVID data visualizations and the quarantine quotidian.Shannon Mattern, Erin Simmons & Emily Bowe - 2020 - Big Data and Society 7 (2).
    In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. (...)
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  • The price of certainty: How the politics of pandemic data demand an ethics of care.Linnet Taylor - 2020 - Big Data and Society 7 (2).
    The Covid-19 pandemic broke on a world whose grip on epistemic trust was already in disarray. The first months of the pandemic saw many governments publicly performing reliance on epidemiological and modelling expertise in order to signal that data would be the basis for justifying whatever population-level measures of control were judged necessary. But comprehensive data has not become available, and instead scientists, policymakers and the public find themselves in a situation where policy inputs determine the data available and vice (...)
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  • Going viral: How a single tweet spawned a COVID-19 conspiracy theory on Twitter.Philip Mai & Anatoliy Gruzd - 2020 - Big Data and Society 7 (2).
    In late March of 2020, a new hashtag, #FilmYourHospital, made its first appearance on social media. The hashtag encouraged people to visit local hospitals to take pictures and videos of empty hospitals to help “prove” that the COVID-19 pandemic is an elaborate hoax. Using techniques from Social Network Analysis, this case study examines how this conspiracy theory propagated on Twitter and whether the hashtag virality was aided by the use of automation or coordination among Twitter users. We found that while (...)
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