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Volume 29 Issue 3
May  2014
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Jung-Tae Lee, Min-Chul Yang, Hae-Chang Rim. Discovering High-Quality Threaded Discussions in Online Forums[J]. Journal of Computer Science and Technology, 2014, 29(3): 519-531. DOI: 10.1007/s11390-014-1446-5
Citation: Jung-Tae Lee, Min-Chul Yang, Hae-Chang Rim. Discovering High-Quality Threaded Discussions in Online Forums[J]. Journal of Computer Science and Technology, 2014, 29(3): 519-531. DOI: 10.1007/s11390-014-1446-5
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Discovering High-Quality Threaded Discussions in Online Forums

  • Department of Computer and Radio Communications Engineering, Korea University, Seoul 136-713, Korea

Funds: This research was partially supported by the Ministry of Knowledge Economy (MKE), Korea, and Microsoft Research through the IT/SW Creative Research Program supervised by the National IT Industry Promotion Agency (NIPA) of Korea under Grant No. NIPA-2012-H0503-12-1012, and the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning of Korea under Grant No. NRF-2012M3C4A7033344.
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  • Author Bio:

    Jung-Tae Lee received the B.S., M.S., and Ph.D. degrees in computer science from Korea University in 2006, 2008, and 2012 respectively. He is currently a researcher at Naver Corporation. His research interests are information retrieval and its applications.

  • Received Date: September 24, 2013
  • Revised Date: December 21, 2013
  • Published Date: May 04, 2014
    Abstract
  • Archives of threaded discussions generated by users in online forums and discussion boards contain valuable knowledge on various topics. However, not all threads are useful because of deliberate abuses, such as trolling and flaming, that are commonly observed in online conversations. The existence of various users with different levels of expertise also makes it diffcult to assume that every discussion thread stored online contains high-quality contents. Although finding high-quality threads automatically can help both users and search engines sift through a huge amount of thread archives and make use of these potentially useful resources effectively, no previous work to our knowledge has performed a study on such task. In this paper, we propose an automatic method for distinguishing high-quality threads from low-quality ones in online discussion sites. We first suggest four different artificial measures for inducing overall quality of a thread based on ratings of its posts. We then propose two tasks involving prediction of thread quality without using post rating information. We adopt a popular machine learning framework to solve the two prediction tasks. Experimental results on a real world forum archive demonstrate that our method can significantly improve the prediction performance across all four measures of thread quality on both tasks. We also compare how different types of features derived from various aspects of threads contribute to the overall performance and investigate key features that play a crucial role in discovering high-quality threads in online discussion sites.
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