Early detection of potential experts in question answering communities

  • Authors:

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

  • Affiliations:

  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

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.