Is this urgent?: exploring time-sensitive information needs in collaborative question answering

ABSTRACT

As online Collaborative Question Answering (CQA) servicessuch as Yahoo! Answers and Baidu Knows are attracting users, questions, and answers at an explosive rate, the truly urgent and important questions are increasingly getting lost in the crowd. That is, questions that require immediate responses are pushed out of the way by the trivial but more recently arriving questions. Unlike other questions in collaborative question answering (CQA) for which users might be willing to wait until good answers appear, urgent questions are likely to be of interest to the asker only if answered in the next few minutes or hours. For such questions, late responses are either not useful or are simply not applicable. Unfortunately, current collaborative question-answering systems do not distinguish urgent questions from the rest, and could thus be ineffective for urgent information needs. We explore text- and data- mining methods for automatically identifying urgent questions in the CQA setting. Our results indicate that modeling the question context (i.e., the particular forum/category where the question was posted) can increase classification accuracy compared to the text of the question alone.

REFERENCES

• 1: Eugene Agichtein , Yandong Liu , Jiang Bian, Modeling information-seeker satisfaction in community question answering, ACM Transactions on Knowledge Discovery from Data (TKDD), v.3 n.2, p.1-27, April 2009 [doi>10.1145/1514888.1514893]
• 2: Jiwoon Jeon , W. Bruce Croft , Joon Ho Lee, Finding similar questions in large question and answer archives, Proceedings of the 14th ACM international conference on Information and knowledge management, October 31-November 05, 2005, Bremen, Germany [doi>10.1145/1099554.1099572]
• 3: Baoli Li , Yandong Liu , Eugene Agichtein, CoCQA: co-training over questions and answers with an application to predicting question subjectivity orientation, Proceedings of the Conference on Empirical Methods in Natural Language Processing, October 25-27, 2008, Honolulu, Hawaii
• 4: D. Zhang and W. S. Lee. Question classification using support vector machines. In Proc. of SIGIR, 2003

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: Question-answering (fact retrieval) systems**

General Terms:
Experimentation, Human Factors

Keywords:
community question answering, social media