Answer quality characteristics and prediction on an academic QandA site: A case study on researchgate

Abstract

Despite various studies on examining and predicting answer quality on generic social QandA sites such as Yahoo! Answers, little is known about why answers on academic QandA sites are voted on by scholars who follow the discussion threads to be high quality answers. Using 1021 answers obtained from the QandA part of an academic social network site ResearchGate (RG), we firstly explored whether various web-captured features and human-coded features can be the critical factors that influence the peer-judged answer quality. Then using the identified critical features, we constructed three classification models to predict the peer-judged rating. Our results identify four main findings. Firstly, responders' authority, shorter response time and greater answer length are the critical features that positively associate with the peer-judged answer quality. Secondly, answers containing social elements are very likely to harm the peer-judged answer quality. Thirdly, an optimized SVM algorithm has an overwhelming advantage over other models in terms of accuracy. Finally, the prediction based on web-captured features had better performance when comparing to prediction on human-coded features. We hope that these interesting insights on ResearchGate's answer quality can help the further design of academic QandA sites.