  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.
