The study analyzes how mental health self‑disclosure on Twitter changed after Elon Musk’s 2022 acquisition, examining 824K tweets related to anxiety, depression, bipolar disorder, borderline personality disorder, and suicidal ideation. Using LIWC and an LLM‑based intimacy classifier, researchers found a post‑acquisition decline in affective and social language, a rise in first‑person singular pronouns and future‑focused expressions, and varied intimacy patterns across conditions—low‑intimacy disclosures increased for anxiety, depression, and BPD, while high‑intimacy disclosures rose for suicide‑related posts. Only the moderate‑versus‑low intimacy decline for BPD remained statistically significant compared to broader Twitter discourse.
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...
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...
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.
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.
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.
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...
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.
Large Language Models (LLMs) are increasingly used for interpersonal advice, influencing how users interpret ambiguous social encounters. Existing Theory of Mind evaluations rely on predefined answers, while uncertainty research overlooks LLMs’ preferred explanations and confidence under psychological distress. To address these gaps, the authors introduce MentalLoom, a novel evaluation of LLM mentalisation comprising 604 ambiguous social episodes, matched depression and social anxiety disclosures, and four‑turn dialogues with unchanged event evidence. Across eight LLMs, disclosures initially elicited more negative explanations overall but more positive narrator‑directed interpretations. Scripted repeated questioning showed greater emotional support, stronger positive narrator‑directed endorsement, and higher certainty without new event evidence, demonstrating that user context can reshape interpretive preferences and commitment while the underlying ambiguity remains unresolved.
The study examines how exposure to distress disclosures in online graduate admissions communities affects users’ psychosocial wellbeing. Analyzing over 1.1 million comments across two Reddit communities, the authors find that users who engage with peer distress during application preparation show higher levels of anxiety, depression, and stress expression, as well as increased commenting activity and lexical redundancy. These effects persist across admissions cycles, but downstream respondents who interact with previously exposed users do not show similar psychosocial changes.
The paper examines how changes made by platforms can disrupt users’ relationships with AI companions. It catalogs 30 disruption events, creates a taxonomy of six types, and identifies three main causes. A risk‑assessment framework with four dimensions is proposed, and a Bayesian time‑series analysis of Reddit data shows that disruptions trigger spikes in anxiety, stress, suicidal expression, and grief, especially when relational continuity and transition support are lacking.
arXiv:2606. 27247v1 Announce Type: new Abstract: In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context.
The study examines how teens interact with companion AI chatbots, revealing that these systems offer comfort, recognition, identity exploration, and relationship rehearsal. However, teens also experience problematic attachment, social substitution, emotional dependence, and disruptions to academic and social life. The findings highlight the need for safety measures that address long‑term relationships, user‑controlled memory, privacy, relational boundaries, and healthy disengagement.