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During the pandemic, the idea that digital adoption would revolutionise audience engagement became widespread in the museum sector. This paper explores whether this is true, utilising tools and debates from the digital humanities to analyse the online content and audiences of a representative sample of 315 museums in the UK.

Our work considers how new advances in pretrained text-to-text generation models might make named entity recognition more accurate, flexible, and streamlined for the digital humanist. We provide an example of how text-to-text generative models can identify mentions of characters, authors, and book names within Goodreads book reviews.

In the last ten years, massive protests against the government and/or unfair elections took place in all three Eastern Slavic countries—Russia (2011/12), Ukraine (2014), and Belarus (2020). In 2022, protesters demonstrated against Russia's invasion of Ukraine. All these protests were mainly organized via social networks, were disseminated in independent media and countered by the official state-owned media. Thus, visual (self-)representations of the protest cultures must be recognized as an integral part of the protests proper. Our project unites close and distant viewing to assess how kaleidoscopic patterns of protest emerge from the constant recombination of specific visual symbols such as banners, flags, slogans, or people marching in the streets. Not only are we interested in a general description of visual and textual (self-)representations of protest in Eastern Europe but we also analyze the patterns of specific protest cultures to describe their symbolic repertoire. In order to achieve this, we build a corpus of visual and textual (self-)representations of protest. We then use deep learning to identify specific symbols in the corpus. These results are utilized to (1) visualize and analyze the differences between official and user-generated content, traditional and new media, and individual countries, and (2) to select specific images and video clips for a qualitative multimodal discourse analysis.