TY - JOUR
T1 - Tracking museums’ online responses to the Covid-19 pandemic: a study in museum analytics
AU - Ballatore, Andrea
AU - Katerinchuk, Val
AU - Poulovassilis, Alexandra
AU - Wood, Peter T
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - museum studies
KW - social media analytics
KW - web analytics
KW - museum analytics
KW - deep learning
KW - COVID19 pandemic
KW - museums during covid
KW - UK museums
KW - cultural analytics
UR - https://doi.org/10.18742/23253329
UR - https://github.com/Birkbeck/museums-in-the-pandemic
M3 - Article
SN - 1556-4673
JO - ACM Journal on Computing and Cultural Heritage
JF - ACM Journal on Computing and Cultural Heritage
ER -