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Enhancing Information Scent: Identifying and Recommending Quality Tags

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2009

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Proceedings of the 2009 ACM International Conference on Supporting Group Work

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Association for Computing Machinery

Abstract

We describe a scenario of tag use and an empirical study of tags as socio-cognitive artifacts providing information scent. We articulated a three-step use scenario of tags, and used it to conceptualize tag quality" as determined by use. We designed and conducted a user study to explore what attributes of tags and taggers predict the user-rated "quality" of tags. We found that frequency best predicted tag quality, while information entropy provided further refinement. We found that people rated our identified quality tags as higher in quality than general tags. But these identified quality tags were not perceived as better than self-generated tags. We derived a regression model for tag quality and discussed implications for social computing."

Description

Zhang, Shaoke; Farooq, Umer; Carroll, John M. (2009): Enhancing Information Scent: Identifying and Recommending Quality Tags. Proceedings of the 2009 ACM International Conference on Supporting Group Work. DOI: 10.1145/1531674.1531676. New York, NY, USA: Association for Computing Machinery. pp. 1–10. Sanibel Island, Florida, USA

Keywords

social bookmarking, sense-making, quality tags

Citation

DOI

10.1145/1531674.1531676

URI

URI

https://dl.eusset.eu/handle/20.500.12015/4858

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GROUP 2009

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Number of citations to item: 7

  • Liuyi Tong, Hui Lin, Pengyi Zhang (2019): “I don't understand it so it can't be good”: Users' acg domain expertise and perceived quality of video tags, In: Proceedings of the Association for Information Science and Technology 1(56), doi:10.1002/pra2.174
  • Neela Sawant, Jia Li, James Z. Wang (2010): Automatic image semantic interpretation using social action and tagging data, In: Multimedia Tools and Applications 1(51), doi:10.1007/s11042-010-0650-8
  • Hesham Allam, Michael Bliemel, Louise Spiteri, James Blustein, Hossam Ali-Hassan (2019): Applying a multi-dimensional hedonic concept of intrinsic motivation on social tagging tools: A theoretical model and empirical validation, In: International Journal of Information Management, doi:10.1016/j.ijinfomgt.2018.11.005
  • U. Cress, C. Held (2012): Harnessing collective knowledge inherent in tag clouds, In: Journal of Computer Assisted Learning 3(29), doi:10.1111/j.1365-2729.2012.00491.x
  • Lei Li, Chengzhi Zhang (2014): Quality evaluation of social tags according to web resource types, In: Proceedings of the 23rd International Conference on World Wide Web, doi:10.1145/2567948.2578998
  • Hyungil Suh, Jeungmin Oh, Wan Chul Yoon (2016): Which Tags Do We Remember in Personal Information Management?, In: International Journal of Human-Computer Interaction 7(32), doi:10.1080/10447318.2016.1181291
  • Hesham Allam, James Blustein, Michael Bliemel, Louise Spiteri (2012): Exploring Factors Impacting Users' Attitude and Intention towards Social Tagging Systems, In: 2012 45th Hawaii International Conference on System Sciences, doi:10.1109/hicss.2012.267
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