Characterizing Cancer Symptom Experiences and Related Psychosocial Concerns Through Chinese Social Media Narratives: Computational Text Analysis

Abstract BackgroundPatients with cancer frequently experience complex and multidimensional symptom burdens. Standardized patient-reported outcome measures provide structured approaches to symptom assessment but may not capture the full range of symptom-related and psychosocial concerns expressed in patients’ narratives. Patient-generated social media data offer a complementary source for examining how these concerns are expressed online. ObjectiveThis study aimed to characterize cancer symptom–related narratives in Chinese social media posts using a conventional symptom assessment framework and to identify additional experiential themes through neural topic modeling. MethodsA computational text analysis integrating large language model (LLM) annotation and neural topic modeling was conducted. A corpus of 26,086 first-person patient narratives was collected from Xiaohongshu and Zhihu between November 2013 and February 2026 using cancer-related search terms. First-person narratives and 6 SPPADE (sleep disturbance, pain, physical function impairment, anxiety, depression, and low energy or fatigue) symptom domains were identified using GPT-4o-mini (OpenAI) and validated against independent human coding. Symptom occurrence, co-occurrence, multiplicity, and coping strategies were characterized, with exploratory stratified analyses by cancer type. BERTopic modeling was applied to identify latent themes, platform sensitivity analyses, and temporal distribution were examined. ResultsFor first-person narrative identification, human raters showed 89.2% agreement (Cohen κ=0.784); the LLM achieved an F1P ConclusionsChinese social media posts contained multidimensional symptom-related narratives alongside psychosocial and experiential concerns that may be less visible in structured symptom assessment. Patient-generated social media data may therefore provide a complementary source for understanding cancer symptom experiences and supportive care concerns. However, differences in platform composition, uneven temporal coverage, and the nonrepresentativeness of social media users limit generalizability, and the clinical utility of integrating such data into routine symptom assessment requires further evaluation.