Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales

TL;DR

This paper addresses interpretability and annotation burden in social-media mental health analysis by proposing MAIMS, a two-step LLM-based framework that uses self-rated mental scales (BDI, INQ-15) to augment analysis. It employs three autoregressive LLMs, $L_{poster}$, $L_{analysis}$, and $L_{discriminator}$, in a discriminator-guided pipeline to produce reliable, explainable inferences. Experiments on Depression_Reddit and IRF datasets show MAIMS achieves state-of-the-art zero-shot performance, with notable gains on IRF and robust ablations validating the value of mental-scale augmentation and the discriminator. The approach demonstrates the potential to infer personality traits from text and offers a scientifically grounded, data-efficient path to mental-health diagnostics, while acknowledging ethical considerations for deployment.

Abstract

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

Figures (2)

• Figure 1: Two-step workflow of MAIMS. Step 1 uses a language model to interpret posts against mental scales, verified by a discriminator. In Step 2, a language model assesses mental health, with a second discriminator confirming the accuracy.
• Figure 2: Ablation Study of Mental Scales: Example