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Journal cover: OCLC Systems & Services

OCLC Systems & Services

ISSN: 1065-075X

Online from: 1985

Subject Area: Library and Information Studies

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Comparing user-generated and librarian-generated metadata on digital images


Document Information:
Title:Comparing user-generated and librarian-generated metadata on digital images
Author(s):Marija Petek, (Department of Library and Information Science and Book Studies, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia)
Citation:Marija Petek, (2012) "Comparing user-generated and librarian-generated metadata on digital images", OCLC Systems & Services, Vol. 28 Iss: 2, pp.101 - 111
Keywords:Digital images, Folksonomy, Images, Information searches, Metadata, Tagging, Tags
Article type:Research paper
DOI:10.1108/10650751211236659 (Permanent URL)
Publisher:Emerald Group Publishing Limited
Abstract:

Purpose – Images can be seen in a different way by different users. The purpose of this paper is to examine how users describe images and to ascertain whether differences exist between users and librarians in creating metadata on images.

Design/methodology/approach – The paper compares metadata on digital images generated by users to metadata generated by librarians. A sample of images taken from Digital Library of Slovenia and Flickr is presented to students to assign tags. The tags are grouped into categories and classes of attributes and compared to keywords added by Slovene librarians and to tags created by Flickr visitors.

Findings – The number of assigned tags differs greatly among survey participants, librarians and Flickr users, the participants being the most productive. A majority of tags reflect perceptual attributes and tagging is mostly done for personal benefit. The matching rate for all images is 41.4 percent; matching is a little higher with the Flickr images.

Practical implications – Social tagging can be used to develop control vocabularies reflecting users' language and to provide access to digital images.

Originality/value – The paper presents quantitative data on image attributes used by users in describing images.



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