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Harnessing global expertise: A comparative study of expertise profiling methods for online communities

Author

Listed:
  • Xiaomo Liu

    (Virginia Tech)

  • G. Alan Wang

    (Virginia Tech)

  • Aditya Johri

    (Virginia Tech)

  • Mi Zhou

    (Xi’an Jiaotong University)

  • Weiguo Fan

    (Virginia Tech)

Abstract

Building expertise profiles in global online communities is a critical step in leveraging the range of expertise available in the global knowledge economy. In this paper we introduce a three-stage framework that automatically generates expertise profiles of online community members. In the first two stages, document-topic relevance and user-document association are estimated for calculating users’ expertise levels on individual topics. We empirically compare two state-of-the-art information retrieval techniques, the vector space model and the language model, with a Latent Dirichlet Allocation (LDA) based model for computing document-topic relevance as well as the direct and indirect association models for computing user-document association. In the third stage we test whether a filtering strategy can improve the performance of expert profiling. Our experimental results using two real datasets provide useful insights on how to select the best models for profiling users’ expertise in online communities that can work across a range of global communities.

Suggested Citation

  • Xiaomo Liu & G. Alan Wang & Aditya Johri & Mi Zhou & Weiguo Fan, 2014. "Harnessing global expertise: A comparative study of expertise profiling methods for online communities," Information Systems Frontiers, Springer, vol. 16(4), pages 715-727, September.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:4:d:10.1007_s10796-012-9385-6
    DOI: 10.1007/s10796-012-9385-6
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    References listed on IDEAS

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    1. Aljukhadar, Muhammad & Senecal, Sylvain, 2016. "The user multifaceted expertise: Divergent effects of the website versus e-commerce expertise," International Journal of Information Management, Elsevier, vol. 36(3), pages 322-332.
    2. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    3. Lin Zhu & Junjie Zhang & Scott W. Cunningham, 2022. "Domain expertise extraction for finding rising stars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5475-5495, September.
    4. Wei Du & Xusen Cheng & Chen Yang & Jianshan Sun & Jian Ma, 2017. "Establishing interoperability among knowledge organization systems for research management: a social network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1489-1506, September.

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