Community-driven question-answering (CQA) services on the Internet let users share content in the form of questions and answers. Usually, questions attract multiple answers of varying quality from other users. A new approach aims to identify high-quality answers from candidate answers to questions that are semantically similar to the new question. Toward that end, the authors developed and tested a quality framework comprising social, textual, and content-appraisal features of user-generated answers in CQA services. Logistic-regression analysis revealed that content-appraisal features were the strongest predictor of quality. These features include dimensions such as comprehensiveness, truthfulness, and practicality. © 2011 IEEE.
CITATION STYLE
John, B. M., Chua, A. Y. K., & Goh, D. H. L. (2011). What makes a high-quality user-generated answer? IEEE Internet Computing, 15(1), 66–71. https://doi.org/10.1109/MIC.2011.23
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