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 trolli...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 difficult 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.展开更多
The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a p...The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a prompt way. In this paper, we apply non-negative matrix factorization and visualization to clustering message data, in order to provide a summary view of messages that disclose their deep semantic relationships. In particular, the NMF is able to find the underlying issues hidden in the messages about which most of the students are concerned. Visualization is employed to estimate the initial number of clusters, showing the relation communities. The experiments and comparison on a real dataset have been reported to demonstrate the effectiveness of the approaches.展开更多
基金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 Founda- tion of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning of Korea under Grant No. NRF-2012M3C4A7033344.
文摘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 difficult 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.
文摘The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a prompt way. In this paper, we apply non-negative matrix factorization and visualization to clustering message data, in order to provide a summary view of messages that disclose their deep semantic relationships. In particular, the NMF is able to find the underlying issues hidden in the messages about which most of the students are concerned. Visualization is employed to estimate the initial number of clusters, showing the relation communities. The experiments and comparison on a real dataset have been reported to demonstrate the effectiveness of the approaches.