Snugi AI model detects early depression from Reddit posts quickly and accurately
Snugi-AI-v2 @ eRisk 2026 Task 2: Early Depression Detection via a Learned Stopping Policy with Sustained Confidence Gate
Computation and LanguageInformation Retrieval
Summary
Detecting signs of depression early using online posts can be tricky because emotions in posts may change quickly. The team behind Snugi-AI-v2 created a software that learns when to stop reading more posts to make a reliable judgement. They use a special gate that only decides someone might be depressed after seeing consistent signals in several posts, helping reduce mistaken alerts. Their approach uses advanced language models to understand discussions and achieved good accuracy while being faster than other teams. They also tested different methods to find the best way to combine timing and confidence.
early depression detectionMLP stopping policyERDE50 metricMentalRoBERTa modelsustained confidence gatefalse positivesrecallF1 scorepolicy learningnatural language processing
Authors
Yuwen Chiu
Abstract
We describe the Snugi-AI-v2 submission to eRisk 2026 Task 2, the second edition of contextualized early depression detection from Reddit discussions. Our central contribution is a learned MLP stopping policy trained to directly optimize ERDE50, replacing the fixed and tiered threshold strategies used in all prior eRisk Task 2 submissions. Combined with a sustained confidence gate that commits only after N=3 consecutive rounds of high policy confidence, the system reduces false positives caused by transient emotional posts without sacrificing recall. The pipeline encodes each discussion thread with a frozen MentalRoBERTa model, maps the accumulated representation to a depression probability via an MLP classifier, and delegates the timing decision to the learned policy. Our best run achieves F1 = 0.73 (Run 1) and F_latency = 0.70 (Runs 0 and 3), with a median alert round of 8 out of 500, completing the full evaluation in 1 hour 26 minutes, the fastest among all complete-submission teams. We report a systematic ablation across five runs spanning two encoder variants, four stopping strategies, and three gate values, along with negative results from GRPO policy training, BDI-II post filtering, MentalLongformer encoding, and DeBERTa ensembling. Code: https://github.com/chiuyuwen91/erisk-2026