Exploring social features for answer quality prediction in CQA portals

The popularity of community based Question Answering (cQA) portals gives rise to the fact that the quality of answer content usually range from very high to very low. In this paper, we exploit social features and topic based features to address a key issue in Chinese cQA portals: predicting the answer quality. Different from previous work, we first investigate and analyze the answers of Haidu Zhidao based on the social features extracted from different aspects. Thereafter, we build a predictive model through machine learning based on the proposed features to make prediction. Extensive experimental results demonstrate the distinguishing ability of social features to predict answer quality. Moreover, we make systematic comparison on different groups of features and find that answer statistic features play a key role in improving the overall performance. In addition, we also find that topic based features outperform word based features a lot.