Characterizing Cancer Symptom Experiences and Related Psychosocial Concerns Through Chinese Social Media Narratives: Computational Text Analysis
Journal of Medical Internet Research ·
Background: Patients 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. Objective: This 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. Methods: A 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. Results: For first-person narrative identification, human raters showed 89.2% agreement (Cohen κ=0.784); the LLM achieved an -score of 81.2% against the adjudicated reference standard. For SPPADE extraction, mean Cohen κ values for LLM-human agreement were 0.714 (SD 0.114) and 0.754 (SD 0.095) across the 6 symptom domains. Overall, 13,896 of 26,086 (53.3%) posts contained at least 1 identifiable symptom. Pain was the most frequently identified symptom (5783/26,086, 22.2%), followed by physical function impairment (4153/26,086, 15.9%) and depression (4050/26,086, 15.5%). Pain and physical function impairment formed the most frequent co-occurring symptom pair (n=1407). Coping strategies were described in 7104/26,086 (27.2%) posts; medication-based approaches were most prominent, particularly for pain and sleep disturbance, while social and psychological support were more common for anxiety and depression. BERTopic modeling identified 13 latent themes, including cancer treatment trajectories, health care system interaction, existential distress, family involvement, and financial burden, highlighting experiential concerns that may be underrepresented in conventional symptom assessment. The proportion of posts containing at least 1 SPPADE symptom was similar between Xiaohongshu and Zhihu (53.2% vs 54.1%; =.35), although individual symptom distributions differed across platforms. Conclusions: Chinese 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.