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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2016: Intelligent Information and Database Systems pp 25–36Cite as

Retrieving Rising Stars in Focused Community Question-Answering

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9622)

Abstract

In Community Question Answering (CQA)‘ forums, there is typically a small fraction of users who provide high-quality posts and earn a very high reputation status from the community. These top contributors are critical to the community since they drive the development of the site and attract traffic from Internet users. Identifying these individuals could be highly valuable, but this is not an easy task. Unlike publication or social networks, most CQA sites lack information regarding peers, friends, or collaborators, which can be an important indicator signaling future success or performance. In this paper, we attempt to perform this analysis by extracting different sets of features to predict future contribution. The experiment covers 376,000 users who remain active in Stack Overflow for at least one year and together contribute more than 21 million posts. One of the highlights of our approach is that we can identify rising stars after short observations. Our approach achieves high accuracy, 85 %, when predicting whether a user will become a top contributor after a few weeks of observation. As a slightly different problem in which we could observe a few posts by a user, our method achieves accuracy higher than 90 %. Our approach provides higher accuracy than baselines methods including a popular time series analysis. Furthermore, our methods are robust to different classifier algorithms. Identifying the rising stars early could help CQA administrators gain an overview of the site’s future and ensure that enough incentive and support is given to potential contributors.

Keywords

  • Random Forest
  • Classification Algorithm
  • Information Seek
  • Random Forest Method
  • Feature Importance

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-662-49390-8_3
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Notes

  1. 1.

    http://highscalability.com/blog/2014/7/21/stackoverflow-update-560m-pageviews-a-month-25-servers-and-i.html.

  2. 2.

    https://archive.org/details/stackexchange.

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Acknowledgments

This work is partially funded by the US National Science Foundation (NSF) BCC-SBE award no. 1244704.

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Authors and Affiliations

  1. Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ, 8854-8019, USA

    Long T. Le

  2. School of Communication and Information, Rutgers University, 4 Huntington Street, New Brunswick, NJ, 08901-1071, USA

    Chirag Shah

Authors
  1. Long T. Le
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  2. Chirag Shah
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Corresponding author

Correspondence to Long T. Le .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Le, L.T., Shah, C. (2016). Retrieving Rising Stars in Focused Community Question-Answering. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_3

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  • DOI: https://doi.org/10.1007/978-3-662-49390-8_3

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