Early Detection of Potential Experts in Question Answering Communities

  • Aditya Pal
  • Rosta Farzan
  • Joseph A. Konstan
  • Robert E. Kraut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

Question answering communities (QA) are sustained by a handful of experts who provide a large number of high quality answers. Identifying these experts during the first few weeks of their joining the community can be beneficial as it would allow community managers to take steps to develop and retain these potential experts. In this paper, we explore approaches to identify potential experts as early as within the first two weeks of their association with the QA. We look at users’ behavior and estimate their motivation and ability to help others. These qualities enable us to build classification and ranking models to identify users who are likely to become experts in the future. Our results indicate that the current experts can be effectively identified from their early behavior. We asked community managers to evaluate the potential experts identified by our algorithm and their analysis revealed that quite a few of these users were already experts or on the path of becoming experts. Our retrospective analysis shows that some of these potential experts had already left the community, highlighting the value of early identification and engagement.

Keywords

Question Answering Potential Experts Expert Identification 

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References

  1. 1.
    Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of Yahoo! answers. In: ACM International Conference on Knowledge Discovery and Data Mining, KDD, pp. 866–874 (2008)Google Scholar
  2. 2.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Journal of Machine Learning, 273–297 (1995)Google Scholar
  3. 3.
    Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: ACM International Conference on Information and Knowledge Management, CIKM, pp. 919–922 (2007)Google Scholar
  4. 4.
    Lawrence, P., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford InfoLab (1999)Google Scholar
  5. 5.
    Pal, A., Konstan, J.A.: Expert identification in community question answering: exploring question selection bias. In: ACM International Conference on Information and Knowledge Management, CIKM, pp. 1505–1508 (2010)Google Scholar
  6. 6.
    Panciera, K., Halfaker, A., Terveen, L.: Wikipedians are born, not made: a study of power editors on Wikipedia. In: ACM International Conference on Supporting Group Work, GROUP, pp. 51–60 (2009)Google Scholar
  7. 7.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  9. 9.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: ACM International Conference on World Wide Web, WWW, pp. 221–230 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aditya Pal
    • 1
  • Rosta Farzan
    • 2
  • Joseph A. Konstan
    • 1
  • Robert E. Kraut
    • 2
  1. 1.Department of Computer Science & EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA

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