arXiv:2608.29010v1 Announce Type: cross
Abstract: People share mental health diagnoses on social media, yet how such language becomes visible around their self-disclosure, and whether community engag...
By Renkai Ma, Lingyao Li, Shanting Chen, Chen Chen, Fan Yang, Yuanyuan Lei
arXiv:2610.35512v1 Announce Type: new
Abstract: Mental health research on social media has been dominated by English-language datasets and DSM-5-centered resources, limiting cross-lingual and cross-c...
By Bingrui Jin, Baihan Li, Zitong Li, Kunyao Lan, Shimeng Li, Kenny Q. Zhu, Mengyue Wu
arXiv:2607. 05685v1 Announce Type: cross Abstract: Large language models are increasingly used as private, always-available conversational systems, but little is known about how people with depressive symptoms use them.
By Neil K. R. Sehgal, Dunigan Folk, Lyle Ungar, Sharath Chandra Guntuku
arXiv:2606. 28334v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) and social media data have led to growing optimism about the ability to detect suicide risk at scale.
By Yaakov Ophir, Ofri Hefetz, Refael Tikochinski, Kfir Bar, Shir Lissak, Shulamit Grinapol, Haya Wachtel, Eyal Fruchter, Roi Reichart
The study examines how eight instruction-tuned large language models (LLMs) handle suicidal ideation (SI) and self‑harm disclosures across three datasets, testing whether informing models of SI context or providing a clinician‑developed response protocol changes their behavior. Results show that baseline elevated risk in model responses is largely due to ignoring the disclosure and refusing to engage; informing models of SI or suppressing refusal increases engagement but does not reliably reduce risk and can even raise it. A clinician‑guided protocol, however, consistently lowers response‑level risk and reduces refusal, highlighting the importance of tailored safety mechanisms for SI contexts.
By Drishti Goel, Soorya Ram Shimgekar, Jeongah Lee, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Koustuv Saha
arXiv:2608.21242v1 Announce Type: new
Abstract: As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates...
By Jiayi Li, Sanjana Menon, Brett Frischmann, Shomir Wilson, Sarah Rajtmajer