Zeeschuimer (“sea foamer”) is a web browser extension from the Digital Methods Initiative in Amsterdam that enables you to collect data while you are browsing social media sites for research and analysis.
It currently works for platforms such as TikTok, Instagram, Twitter and LinkedIn and provides an ndjson file which can be imported into the open source 4CAT: Capture and Analysis Toolkit for analysis.
To make data gathered with Zeeschuimer more accessible for for researchers, reporters, students, and others to work with, we’ve created zeehaven (“sea port”) – a tiny web-based tool to convert ndjson into csv format, which is easier to explore with spreadsheets as well as common data analysis and visualisation software.
Drag and drop a ndjson file into the “sea port” and the tool will prompt you to save a csv file. ✨📦✨
The article explores testing situations – moments in which it is no longer possible to go on in the usual way – across scales during the COVID-19 pandemic through interpretive querying and sub-setting of Twitter data (“data teasing”), together with situational image analysis.
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
The article is available here, and the abstract is as follows:
This article examines the organisation of collaborative digital methods and data projects in the context of engaged research-led teaching in the humanities. Drawing on interviews, field notes, projects and practices from across eight research groups associated with the Public Data Lab (publicdatalab.org), it provides considerations for those interested in undertaking such projects, organised around four areas: composing (1) problems and questions; (2) collectives of inquiry; (3) learning devices and infrastructures; and (4) vernacular, boundary and experimental outputs. Informed by constructivist approaches to learning and pragmatist approaches to collective inquiry, these considerations aim to support teaching and learning through digital projects which surface and reflect on the questions, problems, formats, data, methods, materials and means through which they are produced.