arXiv:2606. 10380v1 Announce Type: cross Abstract: Real-world crisis intervention is inherently conversational, yet existing research largely focuses on static texts.
By Grace Byun, Abigail Lott, Rebecca Lipschutz, Sean T. Minton, Elizabeth A. Stinson, Jinho D. Choi
arXiv:2606. 07226v1 Announce Type: cross Abstract: Human creativity has emerged as a critical competency in the era of large language models.
By Tongzhou Yu, Mingjia Li, Hong Qian, Wenkai Wang, Zongbao Zhang, Yaoyu Jiang, Xiangfeng Wang, Aimin Zhou, Jiajun Guo
arXiv:2609.22696v1 Announce Type: new
Abstract: Decentralized social media platforms create new opportunities and challenges for computational mental health research because data access, moderation,...
By Gaurab Chhetri, Anandi Dutta, Subasish Das
arXiv:2601.09717v2 Announce Type: replace-cross
Abstract: Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but al...
By Yiwei Yan, Guanfeng Liu
The paper reviews how large language models are applied in mental health, covering areas such as social media analysis, clinical conversational agents, therapy support tools, prompt engineering, and multimodal learning. It synthesizes interdisciplinary studies that use social media posts, electronic medical records, and multimodal inputs to detect depression, assess suicide risk, provide personalized therapy, and generate psychoeducational content. The review also discusses advances in model interpretability, annotation strategies, multimodal fusion techniques, and highlights ethical, sociotechnical, and regulatory challenges while proposing frameworks for safe, equitable, and accountable deployment.
By Yisong Chen, Yifan Gao, Sijing Yu, Chuqing Zhao, Yang Lu
The paper presents an LLM-based framework for automatically classifying crisis levels in psychological support hotlines, addressing variability in human judgments and staffing constraints. It introduces a paralinguistic injection method that embeds non‑verbal emotional cues into transcripts, allowing the model to consider acoustic nuances. A reasoning‑enhanced training strategy encourages the model to produce diagnostic reasoning chains, which regularizes and improves classification, achieving a macro F1‑score of 0.802 and accuracy of 0.805 in 5‑fold cross‑validation.
By Terumi Chiba, Yang Luo, Ziyun Cui, Yongsheng Tong, Chao Zhang