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The Wisdom of the Few? “Supertaggers” in Collaborative Tagging Systems

Jared Lorince, Indiana Univeristy Cognitive Science Program and Department of Psychological and Brain Sciences, USA, Sam Zorowitz, Massachusetts General Hospital and Harvard Medical School: Division of Neurotherapeutics, Department of Psychiatry, USA, Jaimie Murdock, Indiana Univeristy Cognitive Science Program and School of Informatics and Computing, USA, Peter M. Todd, Indiana Univeristy Cognitive Science Program, Department of Psychological and Brain Sciences, and School of Informatics and Computing, USA,
 
Suggested Citation
Jared Lorince, Sam Zorowitz, Jaimie Murdock and Peter M. Todd (2015), "The Wisdom of the Few? “Supertaggers” in Collaborative Tagging Systems", The Journal of Web Science: Vol. 1: No. 1, pp 16-32. http://dx.doi.org/10.1561/106.00000002

Published: 04 Aug 2015
© 2015 J. Lorince, S. Zorowitz, J. Murdock, and P. M. Todd
 
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This is published under the terms of CC BY-NC-ND 2.0.

In this article:
1. Introduction
2. Related Work
3. Datasets
4. Identifying “Supertaggers” and Measuring their Influence
5. Differences in Tagging Patterns
6. What Makes a Supertagger?
7. Discussion and Conclusions
References

Abstract

A folksonomy is ostensibly an information structure built up by the “wisdom of the crowd”, but is the “crowd” really doing the work? Tagging is in fact a sharply skewed process in which a small minority of “supertagger” users generate an overwhelming majority of the annotations. Using data from three large-scale social tagging platforms, we explore (a) how to best quantify the imbalance in tagging behavior and formally define a supertagger, (b) how supertaggers differ from other users in their tagging patterns, and (c) if effects of motivation and expertise inform our understanding of what makes a supertagger. Our results indicate that such prolific users not only tag more than their counterparts, but in quantifiably different ways. Specifically, we find that supertaggers are more likely to label content in the long tail of less popular items, that they show differences in patterns of content tagged and terms utilized, and are measurably different with respect to tagging expertise and motivation. These findings suggest we should question the extent to which folksonomies achieve crowdsourced classification via the “wisdom of the crowd”, especially for broad folksonomies like Last.fm as opposed to narrow folksonomies like Flickr.

DOI:10.1561/106.00000002