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Collaborative editing of micro-tags
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Conference on Human Factors in Computing Systems archive
Proceedings of the 27th international conference extended abstracts on Human factors in computing systems table of contents
Boston, MA, USA
SESSION: Spotlight on work in progress session 2 table of contents
Pages 4297-4302  
Year of Publication: 2009
ISBN:978-1-60558-247-4
Authors
Mercan Topkara  IBM T. J. Watson Research Center, Hawthorne, NY, USA
Bernice Rogowitz  IBM T. J. Watson Research Center, Hawthorne, NY, USA
Steve Wood  IBM T. J. Watson Research Center, Hawthorne, NY, USA
Jeff Boston  IBM T. J. Watson Research Center, Hawthorne, NY, USA
Sponsors
ACM: Association for Computing Machinery
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper introduces the InSight system, which was designed to explore two new concepts in social tagging. In this system, we introduce the concept of community-editable tags, a methodology that allows a community of users to edit, modify and delete tags of each other. The goal is to improve the quality of tags, and to reduce the proliferation of incorrect or incomplete tags often found in social networking systems. We also explore the concept of "micro-tagging," which has begun to appear in web-based applications. In "micro-tagging," the user attaches a tag to a subset of large media, such as a segment in a video or a region of an image. InSight allows users to create and edit video micro-tags. Users can mark specific time intervals within a video, and specific spatial locations within video frames, and these tags can be edited by subsequent users. We also present an empirical study which demonstrates an improvement in factual tag quality when the community of users is allowed to edit and delete each others' tags. These results provide a first step in demonstrating how refining tags would make them more valuable for search.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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S. A. Golder and B. A. Huberman. The structure of collaborative tagging systems. Technical report, HP Labs, 2005.
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M. J. Muller, C. Dugan, and D. R. Millen. Metrics for sensemaking in enterprise tag management. CHI Sensemaking Workshop, 2008.
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Collaborative Colleagues:
Mercan Topkara: colleagues
Bernice Rogowitz: colleagues
Steve Wood: colleagues
Jeff Boston: colleagues