Ballatore, Andrea and Katerinchuk, Val and Poulovassilis, Alex and Wood, Peter (2023) Tracking museums' online responses to the Covid-19 pandemic: a study in museum analytics. ACM Journal on Computing and Cultural Heritage , ISSN 1556-4673.
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Ballatore et al - 2023 - Tracking museums online.pdf - Author's Accepted Manuscript Download (1MB) | Preview |
Abstract
The COVID-19 pandemic led to the temporary closure of all museums in the UK, closing buildings and suspending all on-site activities. Museum agencies aim at mitigating and managing these impacts on the sector, in a context of chronic data scarcity. “Museums in the Pandemic” is an interdisciplinary project that utilises content scraped from museums’ websites and social media posts in order to understand how the UK museum sector, currently comprising over 3,300 museums, has responded and is currently responding to the pandemic. A major part of the project has been the design of computational techniques to provide the project’s museum studies experts with appropriate data and tools for undertaking this research, leveraging web analytics, natural language processing, and machine learning. In this methodological contribution, firstly, we developed techniques to retrieve and identify museum official websites and social media accounts (Facebook and Twitter). This supported the automated capture of large-scale online data about the entire UK museum sector. Secondly, we harnessed convolutional neural networks to extract activity indicators from unstructured text in order to detect museum behaviours, including openings, closures, fundraising, and staffing. This dynamic dataset is enabling the museum studies experts in the team to study patterns in the online presence of museums before, during, and after the pandemic, according to museum size, governance, accreditation, and location.
Metadata
| Item Type: | Article |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Peter Wood |
| Date Deposited: | 07 Jun 2023 15:17 |
| Last Modified: | 25 Oct 2023 17:11 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/51338 |
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