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Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
Open accessAbstract
Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales.
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“Structured decoding approaches [65,96,101,115,122] enforce interpretable reasoning pathways by construction, rather than hoping that prompted models will faithfully externalize their computations.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [115]
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“A first group anchors generated explanations directly to questionnaire items or diagnostic criteria, so that each rationale element traces back to a recognized clinical entry such as the Beck Depression Inventory and the DSM-5 [68,71], the PHQ-9 [70], and standardized psychological scales as a class [115].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [115]
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References 4 · Cited by 7
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Metadata
- Authors
- Li, W., Zhu, Y., Lin, X., Li, M., Jiang, Z., & Zeng, Z.
- Affiliations
- South China University of Technology (CN)
- Venue
- Companion Proceedings of the ACM Web Conference 2024
- Publisher
- ACM
- Published
- 2024-05-13
- Pages
- 959-962
- OpenAlex
- W4391988338
- Cited by
- 7
- References
- 4
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 1
- FWCI
- 4.70
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- social media, zero-shot learning, explainable AI, large language models, psychological assessment
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- Li, W., Zhu, Y., Lin, X., Li, M., Jiang, Z., & Zeng, Z. (2024). Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales. Companion Proceedings of the ACM Web Conference 2024. https://doi.org/10.1145/3589335.3651584
