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Enhancing information scent: identifying and recommending quality tags
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Conference on Supporting Group Work archive
Proceedings of the ACM 2009 international conference on Supporting group work table of contents
Sanibel Island, Florida, USA
SESSION: Tagging table of contents
Pages 1-10  
Year of Publication: 2009
ISBN:978-1-60558-500-0
Authors
Shaoke Zhang  The Pennsylvania State University, University Park, PA, USA
Umer Farooq  Microsoft, Redmond, WA, USA
John M. Carroll  The Pennsylvania State University, University Park, PA, USA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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.


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
2
3
4
5
 
6
 
7
Pirolli, P. and Card, S. Information Foraging. Psychological Review, 106, (1999), 643--675.
8
9
 
10
 
11
Cattuto, C., Baldassarri, A., Servedio, V.D.P. and Loreto, V. Vocabulary growth in collaborative tagging systems. Arxiv preprint arXiv:0704.3316, (2007).
 
12
Cattuto, C., Loreto, V. and Pietronero, L. Collaborative Tagging and Semiotic Dynamics. Arxiv preprint cs.CY/0605015, (2006).
 
13
Macgregor, G. and McCulloch, E. Collaborative tagging as a knowledge organisation and resource discovery tool. Library Review, 55, 5, (2006), 291--300.
 
14
Chi, E.H. and Mytkowicz, T. Understanding navigability of social tagging systems. In SigCHI alt.chi (2007).
15
16
17
 
18
Shirky, C. Ontology is overrated. (2005). http://www.shirky.com/writings/ontologyoverrated.html.
 
19
Bush, V. As We May Think. Atlantic Monthly, 176, 1 (1945), 101--108.
 
20
Collins, A. M. and Loftus, E. F. A spreading-activation theory of semantic processing. Psychological Review, 82, 6, (1975), 407--428.
 
21
 
22
Simon, H. A. A behavioral model of rational choice. Quarterly Journal of Economics, 69, (1955), 99--118.
 
23
Klein, G. and Klinger, D. Naturalistic Decision Making. Human Systems IAC Gateway, 11, 3, (1991), 16--19.
 
24
Weick, K. E. Sense-making in organizations. Sage Publications, Newbury Park, CA, 1996.
 
25
Wittgenstein, L. Philosophical Investigations. Blackwell Publishing, MA, 1953.
 
26
Berger, P. L. and Luckmann, T. The Social Construction of Reality: A treatise in the Sociology of Knowledge. Anchor Books, NY, 1966.
27
28
 
29
Vu, K. P. L., Hanley, G. L., Strybel, T. Z. and Proctor, R. W. Metacognitive Processes in Human-Computer Interaction: Self-Assessments of Knowledge as Predictors of Computer Expertise. International Journal of Human-Computer Interaction, 12, 1, (2000), 43--71.
30
31
32
33
34
 
35
Slamecka, N. J. and Graf, P. The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology, 4, 6, (1978), 592--604.
 
36

Collaborative Colleagues:
Shaoke Zhang: colleagues
Umer Farooq: colleagues
John M. Carroll: colleagues