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CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis

Paper · 2026 · Jinyuan Xu, Tian Lan, Xintao Yu et al.

Summary

Openly available Chinese resources for spotting depression risk are scarce, and most offer only yes/no labels without explaining why a user might be at risk. The authors address this gap with CNSocialDepress (CNSD). They built it from Sina Weibo users drawn from the existing SWDD corpus. CNSD covers 233 users and 44,178 posts. Psychology experts marked depression-related text spans and assigned each span to one of six dimensions grounded in DSM-5 and PHQ-9. Three dimensions are "primary" (more objective, clinically specific) and three are "secondary" (subjective or linguistic cues). Each user also receives a six-part structured analysis summary. The annotation followed an expert-in-the-loop protocol, with guidelines refined through random cross-checking. The authors benchmark the data on several tasks: binary classification, generation of structured analyses, summarization, and fine-tuning LLMs for psychological reasoning. They compare BERT-style encoders with open and commercial LLMs. They report that the resource proves useful across these settings. Its value lies in pairing risk labels with professionally validated, interpretable analyses, which existing Chinese datasets lack.

Key points

  • Scale: 116 depressed users (20,360 posts, about 1.02M tokens) and 117 non-depressed users (23,818 posts, about 1.12M tokens). Each user has at least 60 posts and at least 3,000 tokens, counted with the Qwen2.5 tokenizer. In total, 10,306 segments were annotated.
  • The six dimensions are:
    • depressive psychological state
    • medical expressions
    • clinical/somatic symptoms
    • negative emotions
    • external causes
    • depression-related language patterns
  • Annotation was carried out by four senior researchers with psychology and depression expertise, working at span level within each post.
  • The classification baselines were tested on SWDD, WU3D and CNSD. They include:
    • encoders: Chinese-RoBERTa-wwm-ext, BERT-base-chinese and StructBERT-mental
    • open LLMs: Llama3-8b, Glm4-9b-chat, Qwen2.5 7b and DeepSeek-R1 671b
    • commercial LLMs: GPT-4o-Mini and GPT-4o

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Peking UniversityBeijing · China315 works · 43,268 citations🎓Sorbonne UniversitéParis · France34 works · 7,389 citations🎓Beijing Institute of TechnologyBeijing · China43 works · 3,652 citations🎓Beijing Normal UniversityBeijing · China22 works · 3,177 citations🔬Institut de Recherche pour le DéveloppementMarseille · France2 works

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 worksSocial Psychology · 330 works

Cite

Cite as Jinyuan Xu, Tian Lan, Xintao Yu, Xue He, Hezhi Zhang, Ying Wang, Mathieu Valette, Pierre Magistry and Lei Li. 2026. CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis. Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the MENTAL.ai consortium.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/45bbx7jzw7i4

@inproceedings{xu2026cnsocialdepress,
  title = {{CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis}},
  author = {Jinyuan Xu, Tian Lan, Xintao Yu, Xue He, Hezhi Zhang, Ying Wang, Mathieu Valette, Pierre Magistry and Lei Li},
  booktitle = {Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the MENTAL.ai consortium},
  year = {2026},
  doi = {10.63317/45bbx7jzw7i4},
  url = {https://doi.org/10.63317/45bbx7jzw7i4}
}

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