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M.: Tags vs shelves: from social tagging to social classification

By Arkaitz Zubiaga, Christian Körner and Markus Strohmaier

Abstract

Recent research has shown that different tagging motivation and user behavior can effect the overall usefulness of social tagging systems for certain tasks. In this paper, we provide further evidence for this observation by demonstrating that tagging data obtained from certain types of users- so-called Categorizers- outperforms data from other users on a social classification task. We show that segmenting users based on their tagging behavior has significant impact on the performance of automated classification of tagged data by using (i) tagging data from two different social tagging systems, (ii) a Support Vector Machine as a classification mechanism and (iii) existing classification systems such as the Library of Congress Classification System as ground truth. Our results are relevant for scientists studying pragmatics and semantics of social tagging systems as well as for engineers interested in influencing emerging properties of deployed social tagging systems

Topics: Algorithms, Human Factors, Measurement Keywords Tagging, Folksonomies, Classification, Libraries
Publisher: ACM
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.4793
Provided by: CiteSeerX
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