Prompt-based mental health screening from social media text
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Abstract (arXiv)
This article presents a method for prompt-based mental health screening from a large and noisy dataset of social media text. Our method uses GPT 3.5. prompting to distinguish publications that may be more relevant to the task, and then uses a straightforward bag-of-words text classifier to predict actual user labels. Results are found to be on pair with a BERT mixture of experts classifier, and incurring only a fraction of its training costs.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 11 Jan 2024.
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3 of 23 references.
References · 3
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2018SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditionscited by 73
- 2019t-SS3: a text classifier with dynamic n-grams for early risk detection over text streamscited by 4
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Citing papers per year: 2024: 1.
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- 2023Towards Interpretable Mental Health Analysis with Large Language Models
- 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
- 2025A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
- 2026From Baselines to Preferences: A Comparative Study of LoRA/QLoRA and Preference Optimization for Mental Health Text Classification
- 2025Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data
- 2025Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
- 2025Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
- 2025Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice