An article on “Testing and Not Testing for Coronavirus on Twitter: Surfacing Testing Situations Across Scales With Interpretative Methods” has just been published in Social Media + Society. It was co-authored with Noortje Marres, Gabriele Colombo, Liliana Bounegru, Carolin Gerlitz and James Tripp.

In the article we explore testing situations – moments in which it is no longer possible to go on in the usual way – during the COVID-19 pandemic through interpretive querying and sub-setting of Twitter data, followed by situational image analysis:

Interpretative querying, then, does not seek to “resolve something complex into simple elements,” but rather seeks “breaking up, loosening, releasing.” During our workshops, we playfully defined this interpretative data work as “data teasing,” in the sense of “to pluck, pull, tear; pull apart, comb,” and drawing on Haraway’s notion of cat’s cradle as a way to “pass back and forth to each other the patterns-at-stake, sometimes conserving, sometimes proposing and inventing” (Haraway, 2016). Instead of following and counting platform-defined data points, “data teasing” asks us to engage with data and data structures in a more open-ended manner guided by our interpretative concerns.

The full text is available open access here. Further details and links can be found at this project page. The abstract and reference are copied below.

How was testing—and not testing—for coronavirus articulated as a testing situation on social media in the Spring of 2020? Our study examines everyday situations of Covid-19 testing by analyzing a large corpus of Twitter data collected during the first 2 months of the pandemic. Adopting a sociological definition of testing situations, as moments in which it is no longer possible to go on in the usual way, we show how social media analysis can be used to surface a range of such situations across scales, from the individual to the societal. Practicing a form of large-scale data exploration we call “interpretative querying” within the framework of situational analysis, we delineated two types of coronavirus testing situations: those involving locations of testing and those involving relations. Using lexicon analysis and composite image analysis, we then determined what composes the two types of testing situations on Twitter during the relevant period. Our analysis shows that contrary to the focus on individual responsibility in UK government discourse on Covid-19 testing, English-language Twitter reporting on coronavirus testing at the time thematized collective relations. By a variety of means, including in-memoriam portraits and infographics, this discourse rendered explicit challenges to societal relations and arrangements arising from situations of testing and not testing for Covid-19 and highlighted the multifaceted ways in which situations of corona testing amplified asymmetrical distributions of harms and benefits between different social groupings, and between citizens and state, during the first months of the pandemic.

Marres, N., Colombo, G., Bounegru, L., Gray, J. W. Y., Gerlitz, C., & Tripp, J. (2023). Testing and Not Testing for Coronavirus on Twitter: Surfacing Testing Situations Across Scales With Interpretative Methods. Social Media + Society, 9(3). https://doi.org/10.1177/20563051231196538