Predictors of Professional Responses in Nonprofit Mental Health Forums: Interpretable Machine Learning Analysis
Shuang Geng1, Yanghui Li2, Jie Wang2
1College of Management, Shenzhen University, Shenzhen, Guangdong, China.
Background:
Online mental health communities increase access and equity for patients seeking psychological support. User demand and professional contributions are key to their sustainability. While previous research has examined factors influencing physicians' participation in online consultation platforms, limited attention has been given to how post characteristics affect the quantity and length of professional responses in nonprofit mental health communities.
Objective:
This study aims to examine how textual (ie, topic, sentiment, title length, and content length) and contextual (ie, page views and posting time) characteristics of inquiries in nonprofit mental health forums influence the quantity and length of responses from mental health professionals, providing insights for enhancing community interactions.
Methods:
We collected 18,572 question-and-answer records from a Chinese online mental health platform (August 2024-July 2025). Topic features were extracted using BERTopic, and sentiment features were obtained through a distilled Bidirectional Encoder Representations from Transformers-based sentiment classification model. Additional features were derived from post metadata. We compared 5 machine learning models and identified Light Gradient Boosting Machine as the best performer. We then applied Shapley Additive Explanations (SHAP) analysis to it to evaluate the feature contributions to the prediction of response quantity and length.
Results:
In virtual mental health communities, user inquiries fall into 7 topic categories: work, love, depression, boyfriends or girlfriends, school, marriage, and family. Depression-related topics negatively predict response quantity, whereas interpersonal, school, marriage, or family topics are positively correlated. SHAP analysis revealed that page views (SHAP value=0.187) and title length (SHAP value=0.073) are key factors in predicting response quantity, and content length (SHAP value=0.274), sentiment category (SHAP value=0.054), and title length (SHAP value=0.053) are key factors in predicting response length. Posts exhibiting negative emotions are positively related to both the predicted quantity and length of responses, and this effect becomes more pronounced as the degree of emotional intensity increases. Titles with 15-20 characters and content with more than 60 characters are positively correlated with responses, whereas titles with fewer than 7 characters have negative effects. Higher view counts and weekday posts also increase response likelihood.
Conclusions:
This study provides insights into how textual and contextual features of patient posts influence professional responses in nonprofit mental health forums. It enhances understanding of voluntary knowledge contribution behaviors in online mental health communities and offers practical guidance for optimizing platform functional design and user posting strategies. Future researchers are encouraged to address the limitations of this study, which focuses solely on response quantity and length, and to explore details of professional responses, such as by developing a comprehensive measure of response quality.
