Advertisement

Tag and Word Clouds as Means of Navigation Support in Social Systems

  • Martin Leginus
  • Peter Dolog
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8295)

Abstract

Tag cloud is a visual interface that summarizes an underlying data by depicting the most frequent terms (also called as tags) from the dataset. Tags are linked to documents that contain given tags selection. A majority of tag clouds consists of the most frequent tags from a corpus that are alphabetically sorted. However, it has several drawbacks: frequent tags do not have to be relevant for all users, a vast number of terms are semantically similar hence a cloud contains many redundant depictions, an alphabetical sorting of tag cloud does not allow users to discover relations between terms. The objective of this PhD project is to propose, implement and evaluate novel tags selection methods for more relevant, diverse and novel tag clouds. Enhanced relevance of tag clouds should increase the likelihood that user will accomplish a given information retrieval task. Improved diversity and novelty of tag clouds should result into coverage of the entire spectrum of topics from folksonomy resources. Another objective is to expand a set of well-known synthetic metrics (i.e, Coverage, Overlap and Relevance) with new metrics that will capture diversity and novelty of tag clouds. Next ambition is to develop methods for tags clouds generation on top of social networks such as Twitter or Facebook. The objective is to propose words selection methods that will cover as many diverse subtopics from the underlying set of documents, tweets or statuses. The motivation is to minimize the user effort to skip redundant content.

Keywords

Synthetic Metrics Word Cloud Navigation Support Marginal Maximal Relevance Unexpected Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aras, H., Siegel, S., Malaka, R.: Semantic cloud: an enhanced browsing interface for exploring resources in folksonomy systems. In: Workshop on Visual Interfaces to the Social and Semantic Web (VISSW 2010), IUI 2010, Hong Kong, China, February 7 (2009)Google Scholar
  2. 2.
    Begelman, G., Keller, P., Smadja, F.: Automated tag clustering: Improving search and exploration in the tag space. In: Collaborative Web Tagging Workshop at WWW 2006, Edinburgh, Scotland, pp. 15–33. Citeseer (2006)Google Scholar
  3. 3.
    Bernstein, M.S., Suh, B., Hong, L., Chen, J., Kairam, S., Chi, E.H.: Eddi: interactive topic-based browsing of social status streams. In: Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology, pp. 303–312. ACM (2010)Google Scholar
  4. 4.
    Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1998, pp. 335–336. ACM, New York (1998)Google Scholar
  5. 5.
    Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 659–666. ACM, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Durao, F., Dolog, P., Leginus, M., Lage, R.: SimSpectrum: A similarity based spectral clustering approach to generate a tag cloud. In: Harth, A., Koch, N. (eds.) ICWE 2011. LNCS, vol. 7059, pp. 145–154. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Grahl, M., Hotho, A., Stumme, G.: Conceptual clustering of social bookmarking sites. In: Proceedings of I-KNOW, vol. 7, pp. 5–7 (2007)Google Scholar
  8. 8.
    Hassan-Montero, Y., Herrero-Solana, V.: Improving tag-clouds as visual information retrieval interfaces. In: International Conference on Multidisciplinary Information Sciences and Technologies, pp. 25–28. Citeseer (2006)Google Scholar
  9. 9.
    Leginus, M., Dolog, P., Lage, R.: Graph based techniques for tag cloud generation. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media. ACM (2013)Google Scholar
  10. 10.
    Mathes, A.: Folksonomies-cooperative classification and communication through shared metadata. Computer Mediated Communication 47(10) (2004)Google Scholar
  11. 11.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    O’Connor, B., Krieger, M., Ahn, D.: Tweetmotif: Exploratory search and topic summarization for twitter. In: Proceedings of ICWSM, pp. 2–3 (2010)Google Scholar
  13. 13.
    Schrammel, J., Leitner, M., Tscheligi, M.: Semantically structured tag clouds: an empirical evaluation of clustered presentation approaches. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, pp. 2037–2040. ACM (2009)Google Scholar
  14. 14.
    Sinclair, J., Cardew-Hall, M.: The folksonomy tag cloud: when is it useful? J. Inf. Sci. 34, 15–29 (2008)CrossRefGoogle Scholar
  15. 15.
    Sinclair, J., Cardew-Hall, M.: The folksonomy tag cloud: when is it useful? Journal of Information Science 34(1), 15–29 (2008)CrossRefGoogle Scholar
  16. 16.
    Smith, G.: Tagging: people-powered metadata for the social web. New Rider Pr. (2008)Google Scholar
  17. 17.
    Specia, L., Motta, E.: Integrating folksonomies with the semantic web. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 624–639. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Venetis, P., Koutrika, G., Garcia-Molina, H.: On the selection of tags for tag clouds. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 835–844. ACM Press, New York (2011)Google Scholar
  19. 19.
    Zhai, C.X., Cohen, W.W., Lafferty, J.: Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 10–17. ACM, New York (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Martin Leginus
    • 1
  • Peter Dolog
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityAalborg-EastDenmark

Personalised recommendations