Predicting the quality of answers with less bias in online health question answering communities

• We establish an automatic answer quality prediction model that combing semantic features with textual and non-textual features for online health question answering (HQA) communities. • We extend the knowledge adoption model (KAM) theory to obtain textual and non-textual features of answers. • We employ the Bidirectional Encoder Representations from Transformers (BERT) model to get the semantic feature for answer quality prediction. • Experimental results on a real-world dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods on answer quality prediction. Existing approaches in online health question answering (HQA) communities to identify the quality of answers either address it subjectively by human assessment or mainly using textual features. This process may be time-consuming and lose the semantic information of answers. We present an automatic approach for predicting answer quality that combines sentence-level semantics with textual and non-textual features in the context of online healthcare. First, we extend the knowledge adoption model (KAM) theory to obtain the six dimensions of quality measures for textual and non-textual features. Then we apply the Bidirectional Encoder Representations from Transformers (BERT) model for extracting semantic features. Next, the multi-dimensional features are processed for dimensionality reduction using linear discriminant analysis (LDA). Finally, we incorporate the preprocessed features into the proposed BK-XGBoost method to automatically predict the answer quality. The proposed method is validated on a real-world dataset with 48121 question-answer pairs crawled from the most popular online HQA communities in China. The experimental results indicate that our method competes against the baseline models on various evaluation metrics. We found up to 2.9% and 5.7% improvement in AUC value in comparison with BERT and XGBoost models respectively.