Skip to main content

Topic Classification in Social Media Using Metadata from Hyperlinked Objects

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6611))

Abstract

Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks for determining the topic of an individual post. We focus specifically on hyperlinks to objects which have related metadata available on the Web, including Amazon products and YouTube videos. Our experiments show that the inclusion of metadata from hyperlinked objects in addition to the original post content improved classifier performance measured with the F-score from 84% to 90%. Further, even classification based on object metadata alone outperforms classification based on the original post content.

The work presented in this paper has been funded in part by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2).

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: Proceedings of WSDM (2008)

    Google Scholar 

  2. Angelova, R., Weikum, G.: Graph-based text classification: Learn from your neighbors. In: Proceedings of SIGIR (2006)

    Google Scholar 

  3. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: A nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  4. Berendt, B., Hanser, C.: Tags are not metadata, but “just more content”–to some people. In: Proceedings of ICWSM (2007)

    Google Scholar 

  5. Bizer, C., Heath, T., Berners-Lee, T.: Linked Data - The story so far. International Journal on Semantic Web and Information Systems 5(3) (2009)

    Google Scholar 

  6. Figueiredo, F., Belém, F., Pinto, H., Almeida, J., Gonçalves, M., Fernandes, D., Moura, E., Cristo, M.: Evidence of quality of textual features on the Web 2.0. In: Proceedings of CIKM (2009)

    Google Scholar 

  7. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: An update. ACM SIGKDD Exp. 11(1) (2009)

    Google Scholar 

  8. Kinsella, S., Passant, A., Breslin, J.G.: Using hyperlinks to enrich message board content with Linked Data. In: Proceedings of I-SEMANTICS (2010)

    Google Scholar 

  9. Qi, X., Davison, B.: Classifiers without borders: Incorporating fielded text from neighboring web pages. In: Proceedings of SIGIR (2008)

    Google Scholar 

  10. Sun, A., Suryanto, M., Liu, Y.: Blog classification using tags: An empirical study. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 307–316. Springer, Heidelberg (2007)

    Chapter  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

Kinsella, S., Passant, A., Breslin, J.G. (2011). Topic Classification in Social Media Using Metadata from Hyperlinked Objects. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-20161-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20160-8

  • Online ISBN: 978-3-642-20161-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics