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:2606. 19640v1 Announce Type: cross Abstract: AI and large language models (LLMs) have emerged as promising tools to address global mental health challenges.
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.
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.
arXiv:2606. 27247v1 Announce Type: new Abstract: In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context.
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. 31464v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have motivated their adoption across a wide range of domains, including Artificial Intelligence (AI) for mental health.
The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.
CIBuzzBench is a new benchmark that tests cross‑lingual understanding of Chinese internet buzzwords by providing 3,001 buzzwords with English explanations, equivalents, category labels, and harmfulness annotations. The benchmark defines three tasks—Meaning Explanation, Cross‑lingual Equivalent Matching, and Culturally Grounded Harmfulness Detection—to evaluate how well large language models translate and interpret these culturally nuanced terms. Experiments show that current state‑of‑the‑art LLMs still struggle with fine‑grained non‑literal meanings, robust matching, and accurate harmfulness detection across languages.
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,...
APRiL is an open‑ended benchmark designed to evaluate Chinese large language models’ social awareness. It comprises 2,196 evaluation instances across six categories, split into two tasks: Social Scenario Analysis, which tests understanding of social cues, mental states, and conflict structures, and Socially Aware Response, which checks whether models can generate appropriate, natural replies. Experiments on twelve models from six families show a consistent gap between understanding and response quality, indicating that stronger scenario analyses tend to improve response performance.
Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID 19 pandemic, social media has been one of the most used platforms to get the latest news and information related to COVID 19.