ArticleJournal of medical Internet research2026
Characterizing Cancer Symptom Experiences and Related Psychosocial Concerns Through Chinese Social Media Narratives: Computational Text Analysis.
Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.