Creating tag hierarchies for effective navigation in social media

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Creating tag hierarchies for effective navigation in social media

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Conference on Information and Knowledge Management archive
Proceeding of the 2008 ACM workshop on Search in social media table of contents

Napa Valley, California, USA

SESSION: Social network analysis table of contents

Pages 75-82

Year of Publication: 2008

ISBN:978-1-60558-258-0

Authors

K. Selçuk Candan	Arizona State University, Tempe, AZ, USA
Luigi Di Caro	Universita' di Torino, Torino, Italy
Maria Luisa Sapino	Universita' di Torino, Torino, Italy

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

In social media, such as blogs, since the content naturally evolves over time, it is hard or in many cases impossible to organize the content for effective navigation. Thus, one commonly has to resort to simple tools, such as tags and tag clouds, for presenting frequently used keywords to users to provide them at least some high level idea about the content of a given set of social media entries. Most visualizations of tag clouds vary the sizes of the fonts to differentiate important tags from those that are less important. We propose an alternative "contextual-layout" method, TMine, for analyzing and presenting tags that are extracted from textual content. In TMine tags are first mapped onto a latent semantic space. Then, TMine analyzes the relationships between tags relying on an extended boolean interpretation of the semantic space. The tag cloud is condensed into a hierarchy in a way that captures contextual relationships between tags: in particular, descendant terms in the hierarchy occur within the context defined by the ancestor terms. This provides a mechanism for navigation within the tag space as well as for classification of the text documents based on the contextual structure implied by the tags.

REFERENCES

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	1	http://phasetwo.org/post/a-better-tag-cloud.html, 05/31/08.
	2	http://www.usatoday.com, http://www.abcnews.go.com, http://www.forbes.com, http://www.nytimes.com, http://www.smh.com.au, http://www.guardian.co.uk, http://news.bbc.co.uk, http://www.cbsnews.com, http://www.chron.com, http://www.washingtonpost.com, http://news.com.com, http://www.boston.com, and http://www.iht.com
	3	Gouthami Chintalapani , Catherine Plaisant , Ben Shneiderman, Extending the Utility of Treemaps with Flexible Hierarchy, Proceedings of the Information Visualisation, Eighth International Conference, p.335-344, July 14-16, 2004 [doi>10.1109/IV.2004.57]
	4	S. Deerwester, S. Dumais, G.Furnas, R. Harshman, T. Landauer, K. Lochbaum and L. Streeter. Computer Information Retrieval using Latent Semantic Structure, US Patent, 1989.
	5	P. Cimiano, S. Staab, J. Tane. Automatic Acquisition of Taxonomies from Text: FCA meets NLP. ECML/PKDD Workshop on Adaptive Text Extraction and Mining, 2003.
	6	M.F. Cox, M.A.A. Cox. Multidimensional Scaling. Chapman and Hall, 2001.
	7	H. Davulcu, S. Vadrevu, and S. Nagarajan OntoMiner: Bootstrapping and Populating Ontologies From Domain Specific Web Sites SWDB 2003.
	8	Hasan Davulcu , Srinivas Vadrevu , Saravanakumar Nagarajan, OntoMiner: bootstrapping ontologies from overlapping domain specific web sites, Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, May 19-21, 2004, New York, NY, USA [doi>10.1145/1013367.1013545]
	9	Luigi Di Caro , K. Selçuk Candan , Maria Luisa Sapino, Using tagflake for condensing navigable tag hierarchies from tag clouds, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1402021]
	10	B. Fortuna, D. Mladenic, and M. Grobelnik. Visualization of text document corpus. Informatica Journal, 29 (2005), 497--502.
	11	B. Fortuna, M. Grobelnik, and D. Mladenic. System for semi-automatic ontology construction. ESWC, 2006.
	12	B. Fortuna, M. Grobelnik and D. Mladenic. Semi-automatic data-driven ontology construction system KDD, 2006.
	13	C. Eckart and G. Young. The approximation of one matrix by another of lower rank. Psychometrika, I, 211--218, 1936.
	14	Y. Hassan-Montero, V. Herrero-Solana. Improving Tag-Clouds as Visual Information Retrieval Interfaces. Int. Conf. on Multidisciplinary Information Sciences and Technologies, 2006.
	15	Y. Hassan-Montero, V. Herrero-Solana. Interfaz visual para recuperación de información basada en análisis de metadatos, escalamiento multidimensional y efecto ojo de pez El Profesional de la Información 15(4), 2006.
	16	V. Kashyap, C. Ramakrishnan, C. Thomas, D. Bassu, T. C. Rindflesch and A. Sheth TaxaMiner: An Experimentation Framework for Automated Taxonomy Bootstrapping. TR Univ. of Georgia, 2004.
	17	V. Kashyap, C. Ramakrishnan, C. Thomas, D. Bassu, T. C. Rindflesch and A. Sheth TaxaMiner: An Experimentation Framework for Automated Taxonomy Bootstrapping. TR Univ. of Georgia, 2004.
	18	Jong Wook Kim , K. Selcuk Candan , Junichi Tatemura, CDIP: Collection-Driven, yet Individuality-Preserving Automated Blog Tagging, Proceedings of the International Conference on Semantic Computing, p.87-94, September 17-19, 2007 [doi>10.1109/ICSC.2007.19]
	19	A. Jaimes , J. R. Smith, Semi-automatic, data-driven construction of multimedia ontologies, Proceedings of the 2003 International Conference on Multimedia and Expo, p.781-784, July 06-09, 2003
	20	Dawn J. Lawrie , W. Bruce Croft, Generating hierarchical summaries for web searches, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860549]
	21	Yan Qi , K. Selçuk Candan, CUTS: CUrvature-based development pattern analysis and segmentation for blogs and other Text Streams, Proceedings of the seventeenth conference on Hypertext and hypermedia, August 22-25, 2006, Odense, Denmark [doi>10.1145/1149941.1149944]
	22	R. Rada, H. Mili, E.Bicknell, and M. Blettner. Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19(1), 1989.
	23	R. Richardson and A.F. Smeaton. Using WordNet in a Knowledge-Based Approach to Information Retrieval. Working paper CA-1294, Dublin City Univ., Dublin, 1994.
	24	Gerard Salton , Edward A. Fox , Harry Wu, Extended Boolean information retrieval, Communications of the ACM, v.26 n.11, p.1022-1036, Nov. 1983 [doi>10.1145/182.358466]
	25	Jinwook Seo , Ben Shneiderman, A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections, Proceedings of the IEEE Symposium on Information Visualization, p.65-72, October 10-12, 2004 [doi>10.1109/INFOVIS.2004.3]
	26	Hui Yang , Jamie Callan, Ontology generation for large email collections, Proceedings of the 2008 international conference on Digital government research, May 18-21, 2008, Montreal, Canada
	27	Elias Zavitsanos , Georgios Paliouras , George A. Vouros , Sergios Petridis, Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora, Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, p.402-408, November 02-05, 2007 [doi>10.1109/WI.2007.47]