Skip to main content

Analyzing Tag Distributions in Folksonomies for Resource Classification

  • Conference paper
Knowledge Science, Engineering and Management (KSEM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7091))

Abstract

Recent research has shown the usefulness of social tags as a data source to feed resource classification. Little is known about the effect of settings on folksonomies created on social tagging systems. In this work, we consider the settings of social tagging systems to further understand tag distributions in folksonomies. We analyze in depth the tag distributions on three large-scale social tagging datasets, and analyze the effect on a resource classification task. To this end, we study the appropriateness of applying weighting schemes based on the well-known TF-IDF for resource classification. We show the great importance of settings as to altering tag distributions. Among those settings, tag suggestions produce very different folksonomies, which condition the success of the employed weighting schemes. Our findings and analyses are relevant for researchers studying tag-based resource classification, user behavior in social networks, the structure of folksonomies and tag distributions, as well as for developers of social tagging systems in search of an appropriate setting.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbasi, R., Chernov, S., Nejdl, W., Paiu, R., Staab, S.: Exploiting Flickr Tags and Groups for Finding Landmark Photos. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 654–661. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Angelova, R., Lipczak, M., Milios, E., Pralat, P.: Characterizing a social bookmarking and tagging network. In: ECAI 2008 Workshop on Mining Social Data (MSoDa), pp. 21–25. IOS (2008)

    Google Scholar 

  3. Awawdeh, R., Anderson, T.: Improving Search in Tag-Based Systems with Automatically Extracted Keywords. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS, vol. 6291, pp. 378–387. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  4. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann (1998)

    Google Scholar 

  5. Diederich, J., Iofciu, T.: Finding communities of practice from user profiles based on folksonomies. In: 1st International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice (2006)

    Google Scholar 

  6. Godoy, D., Amandi, A.: Exploiting the Social Capital of Folksonomies for Web Page Classification. In: Cellary, W., Estevez, E. (eds.) Software Services for e-World. IFIP AICT, vol. 341, pp. 151–160. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Golder, S., Huberman, B.A.: The structure of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006)

    Article  Google Scholar 

  8. Gupta, M., Li, R., Yin, Z., Han, J.: Survey on social tagging techniques. SIGKDD Explorations 12(1), 58–72 (2010)

    Article  Google Scholar 

  9. Heymann, P., Koutrika, G., Garcia-Molina, H.: Can social bookmarking improve web search? In: WSDM 2008, the International Conference on Web Search and Web Data Mining, pp. 195–206. ACM, New York (2008)

    Chapter  Google Scholar 

  10. Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  11. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: WWW 2008: 17th international Conference on World Wide Web, pp. 675–684. ACM, New York (2008)

    Google Scholar 

  12. Liang, H., Xu, Y., Li, Y., Nayak, R., Tao, X.: Connecting users and items with weighted tags for personalized item recommendations. In: HT 2010: 21st ACM Conference on Hypertext and Hypermedia, pp. 51–60. ACM, New York (2010)

    Google Scholar 

  13. Noll, M.G., Meinel, C.: The metadata triumvirate: Social annotations, anchor texts and search queries. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, vol. 1, pp. 640–647 (2008)

    Google Scholar 

  14. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing and Management 24, 513–523 (1988)

    Article  Google Scholar 

  15. Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: RecSys 2008, pp. 259–266. ACM, New York (2008)

    Google Scholar 

  16. Zubiaga, A., Martínez, R., Fresno, V.: Getting the most out of social annotations for web page classification. In: DocEng 2009: 9th ACM Symposium on Document Engineering, pp. 74–83. ACM, New York (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zubiaga, A., Martínez, R., Fresno, V. (2011). Analyzing Tag Distributions in Folksonomies for Resource Classification. In: Xiong, H., Lee, W.B. (eds) Knowledge Science, Engineering and Management. KSEM 2011. Lecture Notes in Computer Science(), vol 7091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25975-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25975-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25974-6

  • Online ISBN: 978-3-642-25975-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics