Skip to main content

Predicting Post Importance in Question Answer Forums Based on Topic-Wise User Expertise

  • Conference paper
Distributed Computing and Internet Technology (ICDCIT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8956))

Abstract

Q & A forums on the web are aplenty and the content produced through such crowd-sourced efforts is generally of good quality and highly beneficial to novices and experts alike. As the community matures, however, the explosion in the number of posts/answers leads to the information overload problem. Many a times users having expertise in a particular area are not able to address quality issues raised in the area maybe due to the positioning of the question in the list displayed to the user. A good mechanism to assess the quality of questions and to display it to the users depending on their area of expertise, if devised, may lead to a higher quality answers and faster resolutions to the questions posted. In this paper we present the results of our investigations into the effectiveness of various mechanisms to represent user expertise to estimate a post score reflecting its quality/utility of the post. We follow three different approaches to building a user profile representing the user’s areas of expertise: topic models based approach, tag-based approach and semantic user profiling approaches. We present the results of experiments performed on the popular Q&A Forum Stack Overflow, exploring the value add offered by these approaches. The preliminary experiments support our hypothesis that considering additional features in terms of user expertise does offer an increase in the classification accuracy even while ignoring features computable only after the first 24 hours. However, the proposed method to individually leverage on the semantic tag relations to construct an enhanced user profile did not prove beneficial.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Data Mining Reveals the Secret to Getting Good Answers, http://www.technologyreview.com/view/522171/data-mining-reveals-the-secret-to-getting-good-answers/

  2. Correa, D., Sureka, A.: Fit or Unfit: Analysis and Prediction of ‘Closed Questions’ on Stack Overflow. In: Proceedings of the ACM Conference on Online Social Networks. ACM, Boston (2013)

    Google Scholar 

  3. Riahi, F., Zolaktaf, Z., Shafiei, M., Milios, E.: Finding Expert Users in Community Question Answering. In: Proceedings of the 21st International Conference Companion on World Wide Web, Lyon, France, April 16-20, pp. 791–798 (2012)

    Google Scholar 

  4. Yao, Y., Tong, H., Xie, T., Akoglu, L., Xu, F., Lu, J.: Want a Good Answer? Ask a Good Question First!, arXiv:1311.6876 (2013)

    Google Scholar 

  5. http://data.stackexchange.com/stackoverflow/

  6. Fullerton, D.: Stack Exchange Creative Commons data now hosted by the Internet Archive, http://blog.stackexchange.com/category/cc-wiki-dump/ (January 23, 2014)

  7. Correa, D., Sureka, A.: Fit or Unfit: Analysis and Prediction of ‘Closed Questions’ on Stack ‘Overflow’, arXiv:1307.7291 (2013)

    Google Scholar 

  8. Parnin, C., Treude, C., Grammel, L., Storey, M.: Crowd Documentation: Exploring the Coverage and the Dynamics of API Discussions on Stack Overflow, Georgia Tech Technical Report GIT-CS-12-05 (2013)

    Google Scholar 

  9. http://www.kaggle.com/c/predict-closed-questions-on-stack-overflow

  10. http://2013.msrconf.org/challenge.php

  11. Singh, P., Shadbolt, N.: Linked Data in Crowdsourcing Purposive Social Network. In: International World Wide Web Conferences Steering Committee, WWW Companion Volume, pp. 913–918. ACM (2013)

    Google Scholar 

  12. Yang, L., Qiu, M., Gottipati, S., Zhu, F., Jiang, J., Sun, H., Chen, Z.: CQARank: Jointly Model Topics and Expertise in Community Question Answering. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 99–108 (2013)

    Google Scholar 

  13. Riahi, F., Zolaktaf, Z., Shafiei, M., Milios, E.: Finding Expert Users in Community Question Answering. In: Proceedings of the 21st International Conference Companion on World Wide Web, Lyon, France, pp. 791–798 (2012)

    Google Scholar 

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

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Anand, D., Vahab, F.A. (2015). Predicting Post Importance in Question Answer Forums Based on Topic-Wise User Expertise. In: Natarajan, R., Barua, G., Patra, M.R. (eds) Distributed Computing and Internet Technology. ICDCIT 2015. Lecture Notes in Computer Science, vol 8956. Springer, Cham. https://doi.org/10.1007/978-3-319-14977-6_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14977-6_40

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14976-9

  • Online ISBN: 978-3-319-14977-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics