Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study
Collects 294 Reddit posts from women self-identifying as experiencing intimate partner violence, defines eight information-need classes (shelters, legal, police, safety planning, etc.), augments to 2,216 samples with GPT-3.5, and fine-tunes GPT-3.5 for multiclass classification with a per-class training strategy. Reports an F1 of 70.5% on real posts.
Publisher
Journal of Medical Internet Research (JMIR)
Published
12 May 2025
Added
today
DOI
Key Findings
- Fine-tuned GPT-3.5 classified eight domestic-violence information-need categories from forum posts at F1 70.5% (95% CI 60.6-80.4)
- The fine-tuned model outperformed base GPT-3.5/GPT-4 and a fine-tuned Llama 2-7B
- Heavy reliance on synthetic augmentation (294 real → 2,216 samples) is a stated limitation
Methodology Notes
Peer-reviewed, Journal of Medical Internet Research 2025;27:e65397 (12 May 2025), DOI 10.2196/65397. Small real-data base augmented with GPT-3.5-generated samples. jmir.org is JS-rendered to fetchers; verified via Crossref and Europe PMC.
Sources
JMIR article (primary)
Archived snapshot (Wayback Machine) — preserved against link rot
Authors
Shaowei Guan, Vivian Hui, Gregor Stiglic, Rose Eva Constantino, Young Ji Lee, Arkers Kwan Ching Wong
Tags
Cite This
APA
Shaowei Guan et al. (2025). Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study. Journal of Medical Internet Research (JMIR). https://www.jmir.org/2025/1/e65397
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