Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter

  • Fabian Abel
  • Ilknur Celik
  • Geert-Jan Houben
  • Patrick Siehndel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7031)

Abstract

In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial effort. Users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages (tweets) and present different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a large-scale evaluation of faceted search strategies, show significant improvements over keyword search and reveal significant benefits of those strategies that (i) further enrich the semantics of tweets by exploiting links posted in tweets, and that (ii) support users in selecting facet value pairs by adapting the faceted search interface to the specific needs and preferences of a user.

Keywords

faceted search twitter semantic enrichment adaptation 

References

  1. 1.
    Passant, A., Laublet, P.: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data. In: Workshop on Linked Data on the Web, Beijing, China (2008)Google Scholar
  2. 2.
    Passant, A., Hastrup, T., Bojars, U., Breslin, J.: Microblogging: A Semantic Web and Distributed Approach. In: Workshop on Scripting For the Semantic Web, Tenerife, Spain, vol. 368. CEUR-WS.org (2008)Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. 4.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW, pp. 591–600. ACM (2010)Google Scholar
  5. 5.
    Bernstein, M., Kairam, S., Suh, B., Hong, L., Chi, E.H.: A torrent of tweets: managing information overload in online social streams. In: CHI Workshop on Microblogging: What and How Can We Learn From It? (2010)Google Scholar
  6. 6.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Workshop on Web Mining and Social Network Analysis, pp. 56–65. ACM (2007)Google Scholar
  8. 8.
    Jadhav, A., Purohit, H., Kapanipathi, P., Ananthram, P., Ranabahu, A., Nguyen, V., Mendes, P.N., Smith, A.G., Cooney, M., Sheth, A.: Twitris 2.0: Semantically empowered system for understanding perceptions from social data. In: Semantic Web Challenge (2010)Google Scholar
  9. 9.
    Stankovic, M., Rowe, M., Laublet, P.: Mapping Tweets to Conference Talks: A Goldmine for Semantics. In: Workshop on Social Data on the Web, Shanghai, China, vol. 664. CEUR-WS.org (2010)Google Scholar
  10. 10.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: SIGSPATIAL, pp. 42–51. ACM (2009)Google Scholar
  11. 11.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW, pp. 851–860. ACM (2010)Google Scholar
  12. 12.
    Laniado, D., Mika, P.: Making Sense of Twitter. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 470–485. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Letierce, J., Passant, A., Breslin, J., Decker, S.: Understanding how Twitter is used to widely spread scientific messages. In: Web Science Conference (2010)Google Scholar
  14. 14.
    Huang, J., Thornton, K.M., Efthimiadis, E.N.: Conversational Tagging in Twitter. In: Hypertext, pp. 173–178. ACM (2010)Google Scholar
  15. 15.
    Guha, R., Mccool, R., Miller, E.: Semantic search. In: WWW, pp. 700–709. ACM (2003)Google Scholar
  16. 16.
    Teevan, J., Ramage, D., Morris, M.R.: #twittersearch: a comparison of microblog search and web search. In: WSDM, pp. 35–44. ACM (2011)Google Scholar
  17. 17.
    Koren, J., Zhang, Y., Liu, X.: Personalized interactive faceted search. In: WWW, pp. 477–486. ACM (2008)Google Scholar
  18. 18.
    Hearst, M.A.: Design recommendations for hierarchical faceted search interfaces. In: Workshop on Faceted Search Co-located with SIGIR, pp. 26–30 (2006)Google Scholar
  19. 19.
    Oren, E., Delbru, R., Decker, S.: Extending Faceted Navigation for RDF Data. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 559–572. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabian Abel
    • 1
  • Ilknur Celik
    • 1
  • Geert-Jan Houben
    • 1
  • Patrick Siehndel
    • 2
  1. 1.Web Information SystemsDelft University of TechnologyThe Netherlands
  2. 2.L3S Research CenterLeibniz University HannoverGermany

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