Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Usage patterns of collaborative tagging systems
Journal of Information Science
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Can social bookmarking improve web search?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Exploring social annotations for web document classification
Proceedings of the 2008 ACM symposium on Applied computing
Understanding the efficiency of social tagging systems using information theory
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Proceedings of the 2008 ACM workshop on Search in social media
Influences on tag choices in del.icio.us
Proceedings of the 2008 ACM conference on Computer supported cooperative work
The Metadata Triumvirate: Social Annotations, Anchor Texts and Search Queries
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Tagging: People-powered Metadata for the Social Web
Tagging: People-powered Metadata for the Social Web
Getting the most out of social annotations for web page classification
Proceedings of the 9th ACM symposium on Document engineering
Is unlabeled data suitable for multiclass SVM-based web page classification?
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Stop thinking, start tagging: tag semantics emerge from collaborative verbosity
Proceedings of the 19th international conference on World wide web
Of categorizers and describers: an evaluation of quantitative measures for tagging motivation
Proceedings of the 21st ACM conference on Hypertext and hypermedia
The impact of resource title on tags in collaborative tagging systems
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Journal of Information Science
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
The effects of navigation tools on the navigability of web-based information systems
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
Evaluating tag filtering techniques for web resource classification in folksonomies
Expert Systems with Applications: An International Journal
Tag, you can see it!: using tags for access control in photo sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Concurrency and Computation: Practice & Experience
Factors influencing the co-evolution of social and content networks in online social media
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
A self-adapted method for the categorization of social resources
Expert Systems with Applications: An International Journal
How tagging pragmatics influence tag sense discovery in social annotation systems
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
A few thoughts on engineering social machines: extended abstract
Proceedings of the 22nd international conference on World Wide Web companion
Semantic stability in social tagging streams
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
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.