Evaluating answer quality across knowledge domains: Using textual and non-textual features in social Q&A

Abstract

As an increasing important source of information and knowledge, social questioning and answering sites (social Q&A) have attracted significant attention from industry and academia, as they address the challenge of evaluating and predicting the quality of answers on such sites. However, few previous studies examined the answer quality by considering knowledge domains or topics as a potential factor. To fill this gap, a model consisting of 24 textual and non-textual features of answers was developed in this study to evaluate and predict answer quality for social Q&A, and the model was applied to identify and compare useful features for predicting high-quality answers across four knowledge domains, including science, technology, art, and recreation. The findings indicate that review and user features are the most powerful indicators of high-quality answers regardless of knowledge domains, while the usefulness of textual features (length, structure, and writing style) varies across different knowledge domains. In the future, the findings could be applied to automatically assessing answer quality and quality control in social Q&A.