Linking and clustering artworks using social tags: Revitalizing crowd-sourced information on cultural collections

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Social tagging is one of the most popular methods for collecting crowd-sourced information in galleries, libraries, archives, and museums (GLAMs). However, when the number of social tags grows rapidly, using them becomes problematic and, as a result, they are often left as simply big data that cannot be used for practical purposes. To revitalize the use of this crowd-sourced information, we propose using social tags to link and cluster artworks based on an experimental study using an online collection at the Gyeonggi Museum of Modern Art (GMoMA). We view social tagging as a folksonomy, where artworks are classified by keywords of the crowd's various interpretations and one artwork can belong to several different categories simultaneously. To leverage this strength of social tags, we used a clustering method called link communities to detect overlapping communities in a network of artworks constructed by computing similarities between all artwork pairs. We used this framework to identify semantic relationships and clusters of similar artworks. By comparing the clustering results with curators' manual classification results, we demonstrated the potential of social tagging data for automatically clustering artworks in a way that reflects the dynamic perspectives of crowds.
Publisher
WILEY-BLACKWELL
Issue Date
2016-04
Language
English
Article Type
Article
Citation

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, v.67, no.4, pp.885 - 899

ISSN
2330-1635
DOI
10.1002/asi.23442
URI
http://hdl.handle.net/10203/208751
Appears in Collection
HSS-Journal Papers(저널논문)GCT-Journal Papers(저널논문)
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