Mental Health via Writing

Últimos artículos

• LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

  Jiayi Li, Zhaomin Wu, Bingsheng He · 4 de septiembre de 2026

  Tracking depression from multi-session counseling dialogues requires estimating both current symptom severity and how it changes across sessions. Yet progress on this task is constrained by the scarcity of longitudinal counseling data with standardized session-level depression labels. Existing resou…

• Interpretable Symptom Vectors for Depression in a Large Language Model

  Fangyi Zhu, Ajay Subramanian, Allison Constant, Camille Wang, Ravish Gupta, Corey J. Keller · 3 de septiembre de 2026

  Patients with depression present with diverse symptom profiles, yet clinical practice routinely reduces this variation to a single severity score. Large language models (LLMs) can potentially capture various symptoms and their severity from patient speech. However, how depressive symptoms are repres…

• Assessing Suicide Risk in Arabic Crisis Helpline Calls: A Comparison of Arabic and English Large Language Models

  Linhai Ma, Rita El Hachem, Mahatab El Hajj, Lilian Ghandour, Samah Fodeh · 2 de septiembre de 2026

  Crisis helplines assess suicide risk through structured interviews, a process that is slow and dependent on operator training and workload. Natural language processing could support risk assessment and call prioritization, but almost no work addresses Arabic-language helpline calls or operates withi…

• Evidence-Bounded Mental Health Reasoning from Heterogeneous Speech Protocols

  Chengyuan Gao, Jiang Wu, Tao Lu, Jiayan Guo, Mingkun Xu, Tianyi Zang, Shangyang Li · 1 de septiembre de 2026

  Computational mental health screening using multimodal speech and text has shown great promise. However, existing models often assume all clinical speech protocols carry equivalent evidentiary validity. In reality, heterogeneous protocols, from free interviews to fixed reading tasks, support fundame…

• Generating Clinical Vignettes that Preserve Cognitive Formulations

  Amit Oren, Nimrod Hertz-Palmor, Dean Ariel, Guy Laban · 1 de septiembre de 2026

  Large language models can generate fluent clinical case vignettes, but fluency alone does not ensure fidelity to a specifiable clinical structure. We introduce FORMA, a theory-grounded framework that compiles a cognitive model of a disorder into a directed weighted graph, samples a person-specific c…

• Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts

  Andrew Aquilina, Xiang Lorraine Li, Yu-Ru Li · 1 de septiembre de 2026

  Judgments about psychological distress are socially situated: what counts as concerning hinges on community norms around emotional expression, vulnerability, and help-seeking. Yet large language models (LLMs) used for distress detection are typically aligned to a single, undifferentiated standard. H…

• StageWell: A Process-Aligned Chinese Corpus for Positive-Psychology Support Dialogue

  Yuxiong Wang, Ziwei Lin, Bo Wang, Yu Zhang, Shiguang Ni · 1 de septiembre de 2026

  Positive psychology dialogue aims to support emotional distress and positive resource building, requiring models to produce not only empathetic replies but also coherent progression through a multi-turn support process. Existing resources often reduce supervision to turn-level strategies or holistic…

• How Mental Health Self-Disclosure Becomes Visible: Evidence from Eight Conditions on Reddit

  Renkai Ma, Lingyao Li, Shanting Chen, Chen Chen, Fan Yang, Yuanyuan Lei · 1 de septiembre de 2026

  People share mental health diagnoses on social media, yet how such language becomes visible around their self-disclosure, and whether community engagement tracks it, remain unexamined across conditions. We analyze 89,605 Reddit posts from 739 users across eight conditions, removing each user's diagn…

• The Effect of Emotional Context on Large Language Models' Endorsement of Premature Decisions: Comparing Emotional Vulnerability Across Six Commercial Models

  Cheolho Shin, Yoojin Han, Donghun Shin, Kunho Lee · 31 de agosto de 2026

  As large language models (LLMs) are increasingly used for everyday decision-making advice, whether a model shifts the direction of its advice according to the user's emotional state has become an important safety problem. We test whether emotional expression increases a model's endorsement (encourag…

• Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators

  Jonas L\"anzlinger, Katharina O. E. M\"uller, Burkhard Stiller, Bruno Rodrigues · 28 de agosto de 2026

  Depression affects millions worldwide, yet diagnosis relies on subjective self-reports that may miss authentic behavior. This paper presents an approach linking speech acoustics to DSM-5 depressive-behavior indicators through a transparent Linkage Framework. Unlike black-box models, the framework ex…

• Cross-Platform Generalisation Failure in Mental Health Natural Language Processing: A Five-Axis Fairness Audit of Transformer Models on Social Media

  Rajveer Singh Pall, Sameer Yadav · 28 de agosto de 2026

  We introduce the Cross-Platform Fairness Evaluation (CPFE) framework -- a five-axis audit protocol covering discriminative performance, calibration, statistical significance, prediction equity, and attribution stability -- and apply it to four transformer models (BERT, RoBERTa, Emotion-DistilRoBERTa…

• Evidence-Grounded Mapping of Multimodal Human Sensing Psychological Transdiagnostic Dimensions

  Xiyun Hu, Xiangyuan Xue, Yuting Lyu, Hanya Shao, Jingping Nie · 27 de agosto de 2026

  Mobile and wearable sensing enables longitudinal observation of behavior, yet translating these signals into meaningful mental health constructs remains difficult. We introduce a clinician-in-the-loop benchmark for evaluating whether large language models (LLMs) can generate evidence-grounded Brief …

• HealthBench-Psych: A Mental Health Subset of OpenAI's HealthBench

  Matthew Flathers, Phuong Anh Nguyen, Jill Noorily, Julian Herpertz, Meiting Chen, Jasreen Multani, Samuel Powell, Mason Granof, Mark Kalinch, John Torous · 27 de agosto de 2026

  General-purpose health benchmarks increasingly anchor claims about LLM medical performance, but they are not always resolved by clinical specialty, making domain-specific performance hard to isolate. Mental health is of acute public-health concern as millions of people turn to LLMs for psychological…

• HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviews

  Ao Chen, Xiaojiang Peng · 25 de agosto de 2026

  Depression assessment from multimodal clinical interviews requires integrating dispersed evidence from multiple symptoms into a coherent PHQ-8 profile. This process is hierarchical: relevant evidence is often sparse and context-dependent within local question-answer exchanges, multiple exchanges joi…

• All four leading LLMs talk more than they listen to personality-verified synthetic help-seekers

  Pablo A. Fonseca, Raquel Rodr\'iguez-Carvajal, Rafael A. Calvo · 25 de agosto de 2026

  Large language models are increasingly consulted at moments of distress, yet single-turn benchmarks neither test sustained exchanges nor distinguish between users. We built a personality-aware evaluation in which four widely used models advised several synthetic help-seekers, each given a psychometr…

• Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges

  Yisong Chen, Yifan Gao, Sijing Yu, Chuqing Zhao, Yang Lu · 20 de agosto de 2026

  We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate findings from interdisciplinary studies utilizing d…

• Hallucination Span Detection with Input-Side Evidence Alignment

  Miyu Yamada, Yuki Arase · 18 de agosto de 2026

  Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily assess the factuality of an entire generated text, providing limited insight into which output spans are hallucinated or how they relate to the input.…

• North Africa's Missing Framework: NLP-Driven Mental Healthcare in Algeria and Implications for Low-resource Settings

  Meriem Laifa, Abdallah Bengueddoudj · 11 de agosto de 2026

  Mental health disorders are a leading cause of disability worldwide, yet Natural Language Processing (NLP) research for mental healthcare has remained concentrated in high-income, English-language settings. North Africa, and Algeria in particular, is largely absent from this literature despite its u…

• Multi-Perspective Triad Interaction Graph Neural Network for Cognitive Distortion Detection

  Jun Seo Kim, Hye Hyeon Kim · 10 de agosto de 2026

  Cognitive distortion detection is a key task in computational mental health, yet existing approaches often overlook the psychological structure of distorted thoughts. We propose MTI-GNN (Multi-Perspective Triad Interaction Graph Neural Network), which models Beck's cognitive triad---negative views o…

• Natural Language Processing Psychometrics

  Edoardo Sebastiano De Duro, Emma Franchino, Massimo Stella · 10 de agosto de 2026

  Natural Language Processing (NLP) models predicting mental health outcomes rarely specify what they measure: contextual knowledge, emotional content, or syntactic structure. NLP Psychometrics treats psychological prediction from text as a psychometric problem, linking scores to interpretable linguis…

• Automatic Detection of Deaths from Social Networking Sites

  Nuhu Ibrahim, Riza Batista-Navarro · 7 de agosto de 2026

  This dissertation analysed and discussed the differences in linguistic characteristics between pre-mortem and post-mortem social media content, and reported machine learning (ML) classifiers that achieved high performance in automatically detecting deaths of social networking site users from posts a…

• The methodology of Constructing the Large-Scale Dataset for Detecting Presuicidal and Anti-Suicidal Signals in Social Media Texts in Russian

  Igor Buyanov, Darya Yaskova, Danil Serenko, Danil Shkereda, Andrey Yaskov, Ilya Sochenkov · 4 de agosto de 2026

  The suicide is a terrifying act of a person who is misled by his own mental state. This problem arises across many countries. Sadly, Russia also has quite high number of persons who committed suicide. Luckily, a subset of these people writes their struggles in social media, allowing a way to find th…

• Why It Hurts: Identifying the Drivers of Negative Thoughts in Emotional Support Conversations

  Hainiu Xu, Zhaoyue Sun, Hanqi Yan, Jinhua Du, Caroline Catmur, Yulan He · 3 de agosto de 2026

  Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing. This task relies on modifying cognitive appraisals, the subjective interpretation of events that elicit negative emotions, which is typically conceptualized along multiple discrete dim…

• Improving Mental Health Screening and Early Risk Detection in Spanish

  Andreu Casamayor-Segarra, Vicent Ahuir, Antonio Molina-Marco, Lluís-F. Hurtado · 31 de julio de 2026

  Early detection of mental health disorders is often limited by the lack of specialized resources in Spanish and the difficulty of analyzing long histories of social media posts. This paper addresses these challenges through three main contributions. First, we introduce three Spanish foundational mod…

• The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials

  Mila Fodor, Katalin Ócsai, Francesco Periti, Rien Sonck, Alex Boudreau · 31 de julio de 2026

  Depression is a major mental disorder for which diagnosis relies primarily on clinical assessments. Automated methods to support its detection via the psychiatric MADRS scale are getting more and more attention. While existing solutions primarily focus on detecting the disorder from different text s…