Digital Methods Summer School 2022 ‘Vision methodologies: New visual analysis online’ 4-16 July 2022 New Media & Digital Culture Media Studies University of Amsterdam Turfdraagsterpad 9 1012 XT Amsterdam the Netherlands Call for participation The Digital Methods Initiative (DMI), Amsterdam, is holding its annual Summer School on 'Vision methodologies'. The format is that of a (social media and web) data sprint, with tutorials as well as hands-on work for telling stories with data. There is also a programme of keynote speakers. It is intended for advanced Master's students, PhD candidates and motivated scholars who would like to work on (and complete) a digital methods project in an intensive workshop setting. Vision methodologies: Research affordances and critique of new visual analysis online Kate Crawford and Trevor Paglen have produced a withering critique of the data behind computer vision software and other AI applications. In Excavating AI, the project they describe as dataset archaeology, they question the enterprise of image labelling, particularly in the category of ‘persons’, and discuss how sets of labels encompass problematic worldviews. Among the training data sets they scrutinise is ImageNet, the very large set of tagged images with a variety of shocking (and more sensible) labels. In 2019 ImageNet removed images of people, together with their labels, leading to questions of why the images were sourced as they were and labelled as they had been. Despite the removal (and the questions surrounding it) these images and their labels already have downstream effects, they argue, having served to train vision software, among other uses. The critique has opened a debate both within the research communities behind the image sets as well as outside them, asking how to ‘de-bias’ both the training data and the applications that use them as well as whether to label at all (or how to do so fairly). Similar scrutiny has not as yet been made of other aspects of computer vision analytical outputs (and inputs). In the dual effort to both critique and repurpose, the Summer School inquires into the affordances of computer vision for media research, not just the labelling but also especially the study of image circulation via reverse image search as well as the contextual tagging of images, otherwise known as web entities. We also discuss a series of methods to augment automated image analysis through such data enrichment strategies as emoji and hashtag linkage. At the Summer School there are the usual social media tool training tutorials for working on single and cross-platform analysis, but also continued attention to thinking through and proposing how to work critically with social media data, both from mainstream social media platforms as well as so-called alt tech. Apart from the keynotes and the training tutorials, there are also empirical and conceptual projects that participants work on. Projects from the past Summer and Winter Schools include: Detecting Conspiratorial Hermeneutics via Words & Images, Mapping the Fringe on Telegram; Greenwashing, in_authenticity & protest; Searching constructive/authentic posts in media comment sections, Mapping deepfakes with digital methods and visual analytics, “Go back to plebbit”: Mapping the platform antagonism between 4chan and Reddit, Profiling Bolsobots Networks, Infodemic cross-platform analysis, Post-Trump Information Ecology, Streams of Conspirational Folklore, and FilterTube: Investigating echo chambers, filter bubbles and polarization on YouTube. The most recent school had some of the following projects: Climate imaginaries; Repurposing Google Ads; What is a meme, technically speaking?; Tracing the genealogy and change of TikTok audio memes; Google Autocomplete: Racist results still?; and OK Boomer on Twitter. Summer School ’22 organisers: Richard Rogers and Guillen Torres, Media Studies, University of Amsterdam. Application information at https://www.digitalmethods.net <https://www.digitalmethods.net/>. Prof. Richard Rogers Media Studies University of Amsterdam