Answer Quality Prediction for Online Mental Health Q & A Communities Based on BERT Pretraining model

Corresponding author: Cheng Weihan, gfx18238258390@163.com

DOI: 10.12201/bmr.202509.00007

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  Abstract: 〔Abstract〕Purpose / Significance By constructing an online mental health question and answer quality prediction model, the automatic prediction of answer quality is realized, so as to reduce the workload of professional psychologists and the work pressure of content reviewers, improve the efficiency of quality assessment, realize real-time monitoring of answer quality and provide feedback, and help psychologists continuously improve service quality. Method / Process Using 7110 question and answer data from January to June 2023, we fine-tune the BERT pre-training model to extract the deep semantic features of the question and answer text, and then predict the answer quality of online mental health question and answer. At the same time, other text features are integrated to improve the prediction performance of the BERT model. Results / Conclusions The accuracy and F1 value of the fine-tuned BERT model are 0.89, which are better than the four classical models of SVM, XGBOOST, TextCNN and Bi-LSTM in prediction performance. The quality prediction of online mental health Q & A is excellent, and it has the potential to replace manual annotation, content review and real-time monitoring of service quality.

  Key words: Deep Learning; BERT; Online Q&A Community; Mental Health, Service Quality

  Submit time: 1 September 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2025-04-10	10.12201/bmr.202509.00007V1	Download

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