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.
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...
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.
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.
The study investigates how large language models (LLMs) assess psychological distress in online posts from six identity‑based communities. Through a perspectivist annotation task, 321 participants provided 9,587 judgments on 1,198 Reddit posts, revealing modest in‑group agreement (OR = 1.18) that varies across communities. When evaluated against these community‑specific labels, open‑weight LLMs consistently over‑estimate distress—achieving only 31–44% accuracy on posts perceived as none‑to‑mild—while newer models like GPT‑5 and Gemini 2.5 Pro show similar inflation, whereas Claude Opus 4 is more conservative. "whyItMatters":"The findings highlight that miscalibrated distress detection by LLMs can disproportionately impact the very communities they aim to serve, underscoring the need for equitable AI deployment in mental‑health contexts."
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:2409.02244v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human coun...
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.
Early stress detection and risk prediction are crucial for timely mental health assessment and support. While large language models have recently shown promise for social media stress prediction, rely...
The study evaluates prompt evolution techniques for predicting stress and emotion from social media text. Six methods were compared on two model sizes, revealing that GEPA and its extension GEPA+merge consistently improved predictions over initial prompts. Five key prompting strategies—focusing on emotional evidence, distinguishing current from past distress, illustrating indirect cues, requiring emotional context, and guiding label selection—were linked to development gains, while four observations highlighted task-specific and evaluation limitations.
The study examines how intergroup identity—specifically shared gender or age—affects commenters’ social judgments and empathy in the r/AmITheAsshole subreddit. Analyzing 126,716 comments on 36,163 posts, the authors find that ingroup commenters are more likely to give a ‘Not the Asshole’ verdict and display greater text-based understanding, as measured by large language models. The research also notes age differences among commenters and shows that the ingroup effect is moderated by the relationship between the poster and another party mentioned in the conflict.
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...
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...