Identifying and characterizing eating disorder discourse on Chinese social media: a machine learning approach

Eating disorders (EDs) are severe psychiatric conditions with high mortality and substantial medical complications. In China, underdiagnosis and low treatment engagement hinder timely intervention. Social media platforms provide a naturalistic lens into ED-related experiences, yet research on Chinese-language data remains scarce. Advances in machine learning (ML) and deep learning (DL) offer new opportunities to identify and characterize such ED discourse, informing the development of scalable detection methods and culturally tailored prevention and intervention strategies in the Chinese context. We collected ED-related posts from Weibo via keyword-based API searches and manually annotated them into three groups: irrelevant, promotional/educational content, and layperson posts. Five ML/DL methods, including Convolutional Neural Networks (CNNs), Random Forests, XGBoost, Support Vector Machines (SVMs), and Logistic Regression, were trained to identify ED-related posts in a two-stage framework: (1) filtering out irrelevant posts and (2) distinguishing promotional/educational posts from layperson posts. Classifier performance was evaluated on additional posts from the same users. Latent Dirichlet Allocation (LDA) was applied to the layperson subset to extract underlying ED-related themes. CNN consistently outperformed other models, achieving high F1-scores in both classification stages (0.882 and 0.988, respectively). Topic modeling revealed five themes: restrictive symptomatology and physical distress, binge eating and body-health concerns, relapse and coping narratives, emotional venting, and chronic ED patterns with identity impact. This study demonstrates that CNN-based classification combined with topic modeling provides a scalable framework for detecting ED-related discourse on Chinese social media. Beyond methodological advances in non-English NLP, the findings highlight culturally specific symptom expressions and psychosocial concerns, offering novel insights for public health surveillance. These insights can inform the development of early detection tools and culturally sensitive interventions to address the unmet needs of individuals with EDs in China. Eating disorders (EDs) often remain hidden and underdiagnosed in China. Many people share their struggles more openly on social media than in clinical settings. This study employed machine learning to detect ED-related posts on Weibo, a major Chinese social media platform, and then subsequently applied topic modeling to explore themes. We adopted a two-step approach that first removed irrelevant posts and then separated educational content from personal experiences. Among the methods tested, a convolutional neural network (CNN) proved to be the most effective. Using this approach, we analyzed thousands of posts and identified five main themes: restrictive symptoms and physical distress, binge eating and health concerns, relapse and coping, emotional venting, and long-term impacts on daily life and identity. The findings suggest that combining machine learning with social media data can help uncover how people discuss EDs online and may inform early education and prevention efforts in China.