CueSupport-MH: An Explainable Dataset and Interpretable Framework for Mental Health Risk Detection and Response Safety Evaluation

Abstract

Online platforms have become an important medium for individuals to express emotional distress and seek support. Existing mental health NLP resources usually focus on risk classification alone and provide limited support for explaining risk cues or evaluating the safety of responses in conversational settings. This paper introduces CueSupport-MH, a 6,400-instance benchmark constructed from public Reddit conversations and annotated for four complementary dimensions: risk label, risk evidence span, protective cue, and response-safety label. The revised dataset protocol specifies source selection, filtering, deduplication, anonymization, controlled augmentation, split construction, and redistribution constraints. We also propose Trace-MH, a lightweight interpretable framework for joint risk detection and response-safety classification. To avoid privileged-feature comparisons, we report both a text-only operational setting and a gold-span upper-bound setting that uses human evidence annotations only for controlled interpretability analysis. Across five random seeds, Trace-MH obtains a risk macro-F1 of 0.724 in the gold-span setting and 0.676 in the text-only setting, compared with 0.646 for MentalBERT. For response safety, Trace-MH achieves a macro-F1 of 0.752, with harmful-response detection remaining the most difficult class. Ablation studies show that evidence spans, protective cues, contextual features, and joint training each contribute measurable gains, while token-level F1 and intersection-over-union complement cosine similarity for rationale evaluation. The results support CueSupport-MH as a reproducible benchmark for explainable and safety-aware mental health NLP, while also emphasizing that the dataset and models are research tools and not clinical decision systems.