Abstract
Academic social Q&A websites have a lower response quantity than other types of social Q&A. To help academic social Q&A platforms implement mechanisms to improve the quantities of responses to questions that are rarely answered and to predict these quantities, this study uses 93 features representing the linguistic characteristics of academic questions, and compares several methods of prediction to determine the one that delivers the best performance. It also identifies the most useful feature set for such predictions.
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This work was supported by National Social Science Fund Project (No. 19CTQ032).
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Li, L., Li, A., Song, X., Li, X., Huang, K., Ye, E.M. (2020). Predicting Response Quantity from Linguistic Characteristics of Questions on Academic Social Q&A Sites. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_37
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DOI: https://doi.org/10.1007/978-3-030-64452-9_37
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