Early Detection of Potential Experts in Question Answering Communities

  • Aditya Pal
  • Rosta Farzan
  • Joseph A. Konstan
  • Robert E. Kraut
Conference paper

DOI: 10.1007/978-3-642-22362-4_20

Volume 6787 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Pal A., Farzan R., Konstan J.A., Kraut R.E. (2011) Early Detection of Potential Experts in Question Answering Communities. In: Konstan J.A., Conejo R., Marzo J.L., Oliver N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg

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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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