# Prompt-based mental health screening from social media text

arXiv: https://arxiv.org/abs/2401.05912 · HTML: https://mlsanity.com/paper/2401.05912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams](https://mlsanity.com/paper/1911.06147/) (2019)

## Built on by (most cited first, 1 of 1)

- [Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices](https://mlsanity.com/paper/2405.01249/) (2024)

## 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.