Enriching exhibition scholarship

Journal article

Evidence about art works which are shown together and how publics respond remains fragmented across institutions and platforms. Enriching Exhibition Scholarship (EES) combines socially generated texts (newspapers, Twitter, Instagram) with structured museum data using the Linked Art LOUD specifications, and applies text mining, entity reconciliation, and machine learning to automate fine-grained alignment of objects, events, and responses. The Ashmolean Museum held the exhibition Labyrinth, Knossos Myth & Reality, from February to July 2023, we used this opportunity to evaluate the combination of information from catalogue records, and social media. We find that pre-identified “highlights” dominate social mentions; institutional hashtags are widely adopted, while external tags and influential users amplify reach, suggesting opportunities to leverage ambient trends. Semi-automation reduces the labour of gathering exhibition-level evidence while preserving descriptive richness. The resulting interconnected corpus supports provenance research, studies of style transmission, inter-museum network analysis, and longitudinal tracking of exhibition themes by venue and time. All workflows are openly documented, and data are published as openly as possible to promote reuse and incentivise broader data release, fostering cross-sector uptake and sustainable practice in museum contexts.

Authors: Llewellyn, C; Shapland, A; Bonnet, T; Sanderson, R; Page, K

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Digital Scholarship in the Humanities
• Volume: 41
• Issue: S1
• Pages: i188–i195
• Publication date: 2025-11-09
• Acceptance date: 2025-01-16
• DOI: 10.1093/llc/fqaf074
• EISSN: 2055-768X
• ISSN: 2055-7671
• Language: English
• Keywords: Large Language Models; exhibitions; Linked Open Data; social media; information retrieval; Linked Art