Research Project

Developing data analysis, visualization and digital storytelling solution to explore GLAM digital soft power

GLAM and Digital Soft Power in the Post-pandemic World

SNU Conference Mapping and Theorizing Hallyu

Galleries, libraries, archives, and museums (GLAMs) have evolved beyond their traditional role of preserving cultural heritage. In the 21st century, they've become key players in the creative economy and wield soft power by influencing place branding, urban regeneration and tourism development. The COVID-19 pandemic has further transformed them into "hybrid spaces," blending physical and virtual experiences through augmented reality, virtual reality, and digital tools. Despite their growing global influence, there's a lack of consensus on how to measure this soft power or assess its impact. This hinders strategic development, especially in the post-pandemic world with its opportunities yet challenges for international relations.


The research team aimed to bridge this gap using data analysis, geo-visualization, and machine learning. Working with many museums in Singapore, Melbourne, and London, the project developed innovative methods to understand, measure, and predict GLAMs' soft power. Through interactive global online activities, the project created a prototype model that is able to visualize and map soft power of GLAM institutions to allow them to strategically leverage their cultural resources for a more accurate impact analysis and proactive planning and development.

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Designing Data to Power prototype

Employing data-driven approaches, geo-visualisation and machine learning, this project aimed to provide a solution by designing a tool for automatic GLAM data aggregation to visualise their global outreach and influence in different countries. The project built on the award-winning pilot project Museum Soft Power Map, developed in collaboration with the Australian Centre for the Moving Image in 2018. The new solution significantly advanced the pilot application by applying new approaches to data collection, analysis and visualization.



From labour-intensive manual data collection to automatic data aggregation

Data mining and aggregation, tapping into open access data

From a static visualisation of data to a dynamic mapping

Developing a mapping app with interface for a dynamic data update

From accumulative data mapping to time-space data visualization

Developing a timeline

From data visualization to data intelligence

Employing machine learning and AI to analyze patterns of cultural engagement

Activities

Drawing on collaborations and consultations with 25 museum professionals from Europe and Asia-Pacific, including the Victoria & Albert Museum in London, the Museum of Asian Civilisations in Singapore and the Australian Museum in Canberra, the project explored illuminating provocative examples of GLAM digital soft power effects. It also generated knowledge through critical reflexive praxis, bringing together over 200 museum professionals, policy makers and digital creatives through a series of online research sessions, webinars and datathons. Watch video materials of all sessions and explore more below.

Global circulation of collections and exhibitions in immersive environments

Online Session 1
Opportunities

Global circulation of collections and exhibitions in immersive environments

  • Datathon: Mapping the potential appeal of heritage collections: From API to geo-visualization
  • Webinar: GLAM collections and exhibitions in immersive environments
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Communicating to audiences in the global media environment

Online Session 2
Risks and Challenges

Communicating to audiences in the global media environment

  • Datathon: Understanding and mapping digital museum audiences
  • Webinar: Global communications and audiences: Propaganda, fake news & AI censorship
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Digital GLAM and Urban Soft Power

Online Session 3
Zooming In

Digital GLAM and Urban Soft Power

  • Datathon: Forecasting local impacts of travelling exhibitions
  • Webinar: Urban Data Infrastructures and Digital Place-making
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