Expert identification in Community Question Answering: Exploring question selection bias

Aditya Pal, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

64 Scopus citations

Abstract

Community Question Answering (CQA) services enables users to ask and answer questions. In these communities, there are typically a small number of experts amongst the large population of users. We study which questions a user select for answering and show that experts prefer answering questions where they have a higher chance of making a valuable contribution. We term this preferential selection as question selection bias and propose a mathematical model to estimate it. Our results show that using Gaussian classification models we can effectively distinguish experts from ordinary users over their selection biases. In order to estimate these biases, only a small amount of data per user is required, which makes an early identification of expertise a possibility. Further, our study of bias evolution reveals that they do not show significant changes over time indicating that they emanates from the intrinsic characteristics of users.

Original languageEnglish (US)
Title of host publicationCIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
Pages1505-1508
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada
Duration: Oct 26 2010Oct 30 2010

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
CountryCanada
CityToronto, ON
Period10/26/1010/30/10

Keywords

  • Expert identification
  • Question answering
  • Selection bias

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