Text Classification with Limited Training Data: Suicide Risk Detection on Social Media

In this work, we evaluate the effectiveness of several machine learning models for text classification on small datasets, focusing on a collection of Reddit posts labeled for suicidal behavior. Unlike with larger datasets, where fine-tuning complex models is typically effective, we demonstrate that carefully engineered prompts can achieve superior classification accuracy when training data is limited. Our findings highlight the potential of prompt-based approaches for effective use in resource-constrained scenarios, offering insights for researchers tackling similar small dataset challenges in text classification.