Heritage site-seeing through the visitor’s lens on Instagram

Tania Loke, Yayoi Teramoto, Chico Q. Camargo, Kathryn Eccles

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


English Heritage is a charity that manages over 400 historic sites in the UK, from prehistoric sites to medieval castles, most of them free, non-ticketed, and unstaffed. As such, there is little information about visitor attendance and behaviour in those sites—a challenge common to other non-ticketed heritage sites. In this context, image-based social media such as Instagram appear as a possible solution, as photographs are often central to the tourist experience, and tourists present their imagined audiences with a self-narrative of their trip. Therefore, this study aims to improve our understanding of tourist behaviour in unstaffed heritage sites by analysing publicly available Instagram data. We collect posts on unstaffed English Heritage sites, finding that posting activity concentrates at a few sites. Focusing on 3,979 images each for the top five sites, we analyse image content using pre-trained object detection models. Besides off-the-shelf inference, we fine-tune a model to identify structures from particular heritage sites, and are able to describe the types of photographs taken by visitors in each site, supporting the notion of tourists as performers with the site serving as backdrop. Overall, this study demonstrates a methodology for understanding cultural behaviour at heritage sites using images from social media posts. In addition to recovering the otherwise lost connection between a heritage organisation and its visitors, our methodology can be readily extended to other tourist destinations to understand how visitors interact with and relate to these sites and the objects within them through their photographs.

Original languageEnglish
JournalJournal of Cultural Analytics
Issue number3
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported by a Knowledge Exchange Fellowship from The Oxford Research Centre in the Humanities (TORCH) at the University of Oxford, Grant number 0005946.

Publisher Copyright:
© 2022, Department of Languages, Literatures, and Cultures, McGill University. All rights reserved.


  • computer vision
  • cultural heritage
  • instagram
  • object detection
  • social media analysis
  • tourism


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