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Social tags: meaning and suggestions
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Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents
Napa Valley, California, USA
SESSION: IR: social search table of contents
Pages 223-232  
Year of Publication: 2008
ISBN:978-1-59593-991-3
Authors
Fabian M. Suchanek  Max-Planck Institute for Informatics, Saarbrücken, Germany
Milan Vojnovic  Microsoft Research Cambridge, Cambridge, United Kingdom
Dinan Gunawardena  Microsoft Research Cambridge, Cambridge, United Kingdom
Sponsors
ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper aims to quantify two common assumptions about social tagging: (1) that tags are "meaningful" and (2) that the tagging process is influenced by tag suggestions. For (1), we analyze the semantic properties of tags and the relationship between the tags and the content of the tagged page. Our analysis is based on a corpus of search keywords, contents, titles, and tags applied to several thousand popular Web pages. Among other results, we find that the more popular tags of a page tend to be the more meaningful ones. For (2), we develop a model of how the influence of tag suggestions can be measured. From a user study with over 4,000 participants, we conclude that roughly one third of the tag applications may be induced by the suggestions. Our results would be of interest for designers of social tagging systems and are a step towards understanding how to best leverage social tags for applications such as search and information extraction.


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.

 
1
D. C. Boes. On the estimation of mixing distributions. The Annals of Mathematical Statistics, 37(1):177--188, 1966.
 
2
E. H. Chi, A. Kittur, and T. Mytkowicz. Augmented social cognition: Understanding social foraging and social sensemaking. In Proc. of the HCIC 2007 Winter Workshop, Fraser, Colorado, Jan 31-Feb 4 2007.
3
 
4
C. Fellbaum, editor. WordNet: An Electronic Lexical Database. MIT Press, 1998.
 
5
6
 
7
 
8
G. A. Miller. The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63:81--97, 1956.
 
9
E. Santos-Neto, M. Ripeanu, and A. Iamnitchi. Tracking user attention in collaborative tagging communities. In Workshop on Contextualized Attention Metadata. ACM Press, 2007.
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11
12
13
 
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M. Vojnović, J. Cruise, D. Gunawardena, and P. Marbach. Ranking and suggesting tags in collaborative tagging applications. Technical Report MSR-TR-2007-06, Microsoft Research, February 2007.
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Z. Xu, Y. Fu, J. Mao, and D. Su. Towards the semantic web: Collaborative tag suggestions. In Proc. of the Workshop on Collaborative Web Tagging at WWW 2006, 2006.


Collaborative Colleagues:
Fabian M. Suchanek: colleagues
Milan Vojnovic: colleagues
Dinan Gunawardena: colleagues