Building a Microblog Corpus for Search Result Diversification

  • Ke Tao
  • Claudia Hauff
  • Geert-Jan Houben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8281)


Queries that users pose to search engines are often ambiguous - either because different users express different query intents with the same query terms or because the query is underspecified and it is unclear which aspect of a particular query the user is interested in. In the Web search setting, search result diversification, whose goal is the creation of a search result ranking covering a range of query intents or aspects of a single topic respectively, has been shown in recent years to be an effective strategy to satisfy search engine users. We hypothesize that such a strategy will also be beneficial for search on microblogging platforms. Currently, progress in this direction is limited due to the lack of a microblog-based diversification corpus. In this paper we address this shortcoming and present our work on creating such a corpus. We are able to show that this corpus fulfils a number of diversification criteria as described in the literature. Initial search and retrieval experiments evaluating the benefits of de-duplication in the diversification setting are also reported.


Relevance Judgment Retrieval Experiment Query Intent Link Open Data Cloud Query Likelihood 
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.


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  1. 1.
    Clarke, C.L.A., Craswell, N., Soboroff, I.: Overview of the trec 2009 web track. In: TREC 2009 (2009)Google Scholar
  2. 2.
    Carterette, B., Chandar, P.: Probabilistic models of ranking novel documents for faceted topic retrieval. In: CIKM 2009, pp. 1287–1296 (2009)Google Scholar
  3. 3.
    Slivkins, A., Radlinski, F., Gollapudi, S.: Learning optimally diverse rankings over large document collections. In: ICML 2010, pp. 983–990 (2010)Google Scholar
  4. 4.
    Santos, R.L.T., Macdonald, C., Ounis, I.: Intent-aware search result diversification. In: SIGIR 2011, pp. 595–604 (2011)Google Scholar
  5. 5.
    Santos, R.L.T., Macdonald, C., Ounis, I.: Aggregated search result diversification. In: Amati, G., Crestani, F. (eds.) ICTIR 2011. LNCS, vol. 6931, pp. 250–261. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Teevan, J., Ramage, D., Morris, M.R.: #TwitterSearch: a comparison of microblog search and web search. In: WSDM 2011, pp. 35–44 (2011)Google Scholar
  7. 7.
    Tao, K., Abel, F., Hauff, C., Houben, G.J., Gadiraju, U.: Groundhog day: Near-duplicate detection on twitter. In: WWW 2013, pp. 1273–1284 (2013)Google Scholar
  8. 8.
    Cronen-Townsend, S., Croft, W.B.: Quantifying query ambiguity. In: HLT 2002, pp. 104–109 (2002)Google Scholar
  9. 9.
    Bennett, P.N., Carterette, B., Chapelle, O., Joachims, T.: Beyond binary relevance: preferences, diversity, and set-level judgments. SIGIR Forum 42(2), 53–58 (2008)CrossRefGoogle Scholar
  10. 10.
    Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998, pp. 335–336 (1998)Google Scholar
  11. 11.
    Zhai, C., Lafferty, J.: A risk minimization framework for information retrieval. Inf. Process. Manage. 42(1), 31–55 (2006)CrossRefzbMATHGoogle Scholar
  12. 12.
    Yue, Y., Joachims, T.: Predicting diverse subsets using structural svms. In: ICML 2008, pp. 1224–1231 (2008)Google Scholar
  13. 13.
    Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM 2009, pp. 5–14 (2009)Google Scholar
  14. 14.
    Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: SIGIR 2008, pp. 659–666 (2008)Google Scholar
  15. 15.
    Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: CIKM 2009, pp. 621–630 (2009)Google Scholar
  16. 16.
    Clarke, C.L.A., Kolla, M., Vechtomova, O.: An effectiveness measure for ambiguous and underspecified queries. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 188–199. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Tao, K., Abel, F., Hauff, C., Houben, G.J.: What makes a tweet relevant for a topic? In: #MSM2012 Workshop, pp. 49–56 (2012)Google Scholar
  18. 18.
    Golbus, P., Aslam, J., Clarke, C.: Increasing evaluation sensitivity to diversity. Information Retrieval, 1–26 (2013)Google Scholar
  19. 19.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRefGoogle Scholar
  20. 20.
    Zhai, C.X., Cohen, W.W., Lafferty, J.: Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: SIGIR 2003, pp. 10–17 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ke Tao
    • 1
  • Claudia Hauff
    • 1
  • Geert-Jan Houben
    • 1
  1. 1.Web Information SystemsTU DelftDelftThe Netherlands

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