Abstract
There have been increasing interests in Community Question Answering (CQA) recently. CQA websites such as Yahoo! Answers and Baidu Knows are increasingly popular, attracting tens of thousands of users to submit questions and answers every day. However, we find that there is a gap in the study of what kinds of questions are more likely to attract answers, which makes sense for users in practice when asking questions in the CQA systems. In this paper, we investigate the factors that may affect users’ questions to attract answers. We also introduce a general framework to predict how many replies a question is expected to receive. Evaluation results of the framework on real data prove its effectiveness.
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Quan, X., Wenyin, L. (2012). Analyzing Question Attractiveness in Community Question Answering. In: Ding, W., Jiang, H., Ali, M., Li, M. (eds) Modern Advances in Intelligent Systems and Tools. Studies in Computational Intelligence, vol 431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30732-4_18
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DOI: https://doi.org/10.1007/978-3-642-30732-4_18
Publisher Name: Springer, Berlin, Heidelberg
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