A team collaboration space supporting capture and access of virtual meetings
GROUP '01 Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work
The Journal of Machine Learning Research
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Wikinomics: How Mass Collaboration Changes Everything
Wikinomics: How Mass Collaboration Changes Everything
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
SparTag.us: a low cost tagging system for foraging of web content
AVI '08 Proceedings of the working conference on Advanced visual interfaces
IBM multimedia analysis and retrieval system
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A "live" interactive tagging interface for collaborative learning
CDVE'11 Proceedings of the 8th international conference on Cooperative design, visualization, and engineering
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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.