A Comparative Analysis of Peer Support in Forum-based and Chat-based Mental Health Communities: Technical-Structural-Functional Model of Social Support
Online support communities have become vital spaces offering varied forms of support to individuals facing mental health challenges. Despite the proliferation of platforms with distinct technical structures, little is known about how these features shape support dynamics and the socio-technical mechanisms at play. This study introduces a technical-structural-functional model of social support and systematically compares communication network structures and support types in 20 forum-based and 20 chat-based mental health communities. Using supervised machine learning and social network analysis, we find that forum-based communities foster more informational and emotional support, whereas chat-based communities promote greater companionship. These patterns were partially explained by network structure: higher in-degree centralization in forums accounted for the prevalence of informational support, while decentralized reply patterns in chat groups accounted for more companionship. These findings extend the structural-functional model of support to online contexts and provide actionable guidance for designing support communities that align technical structures with users' support needs.
A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots
University of Illinois Urbana-Champaign, Urbana, United States（伊利诺伊大学厄巴纳-香槟分校）；Indiana University Indianapolis, Indianapolis, United States（印第安纳大学印第安纳波利斯分校）
Conversational chatbots are Artificial Intelligence (AI)-powered applications that assist users with various tasks by responding in natural language and are prevalent across different industries. Most of the chatbots that we encounter on websites and digital assistants such as Alexa, Siri does not express empathy towards the user, and their ability to empathise remains immature. Lack of empathy towards the user is not critical for a transactional or interactive chatbot, but the bots designed to support mental healthcare patients need to understand the emotional state of the user and tailor the…
Pattern Recognition Center, WeChat AI, Tencent Inc（腾讯公司微信人工智能模式识别中心）；University of Chinese Academy of Sciences（中国科学院大学）
Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics. For a chatbot which responds to millions of online users including minors, we argue that it should have a healthy mental tendency in order to avoid the negative psychological impact on them. In this paper, we establish several mental health assessment dimensions for chatbots (depression, anxiety, alcohol addiction, empathy) and introduce the questionnaire-based mental health assessment methods. We…
Cloning the Self for Mental Well-Being: A Framework for Designing Safe and Therapeutic Self-Clone Chatbots
University of British Columbia（不列颠哥伦比亚大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；City College of New York（纽约市立学院）；Korea Institute of Science and Technology（韩国科学技术院）
From Risk Avoidance to User Empowerment in AI Mental Health Crisis Support
Dartmouth College（达特茅斯学院）；Northwestern University（西北大学）；University of California San Francisco（加州大学旧金山分校）；University of Minnesota（明尼苏达大学）；Microsoft Research（微软研究院）；LivingWorks；Florida State University（佛罗里达州立大学）；Microsoft（微软）；Boston University（波士顿大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Mental Health America（美国心理健康协会）；University of Washington（华盛顿大学）
A clinically validated framework for auditing AI chatbot behavior in mental health interactions
Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克-伦敦大学学院计算精神病学与老龄化研究中心）；Sydney Medical School, University of Sydney（悉尼大学悉尼医学院）；UK AI Security Institute（英国人工智能安全研究所）；Department of Experimental Psychology, University of Oxford（牛津大学实验心理学系）；Microsoft AI（微软人工智能）；Department of Psychiatry, University of Oxford（牛津大学精神病学系）
Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）
Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools
Sue & Bill Gross School of Nursing, University of California, Irvine（加州大学欧文分校苏与比尔·格罗斯护理学院）；Department of Computer Sciences, University of California, Irvine（加州大学欧文分校计算机科学系）；School of Medicine, University of California, Irvine（加州大学欧文分校医学院）；Department of Psychological Science, University of California, Irvine（加州大学欧文分校心理科学系）；Chicago School of Professional Psychology（芝加哥专业心理学院）；HealthUnity
Tsinghua University（清华大学）；Beijing Normal University（北京师范大学）
The growing demand for mental health support has highlighted the importance of conversational agents as human supporters worldwide and in China. These agents could increase availability and reduce the relative costs of mental health support. The provided support can be divided into two main types: cognitive and emotional support. Existing work on this topic mainly focuses on constructing agents that adopt Cognitive Behavioral Therapy (CBT) principles. Such agents operate based on pre-defined templates and exercises to provide cognitive support. However, research on emotional support using such…
Mobile mental health applications are seen as a promising way to fulfill the growing need for mental health care. Although there are more than ten thousand mental health apps available on app marketplaces, such as Google Play and Apple App Store, many of them are not evidence-based, or have been minimally evaluated or regulated. The real-life experience and concerns of the app users are largely unknown. To address this knowledge gap, we analyzed 2159 user reviews from 117 Android apps and 2764 user reviews from 76 iOS apps. Our findings include the critiques around inconsistent moderation stan…
Co-Designing AI For Mental Health Support With Young Adults of Color (YOC): Needs, Expectations, and Implications for AI Literacy
University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）；University of Cincinnati（辛辛那提大学）；University of Washington（华盛顿大学）；Center for Democracy & Technology（民主与技术中心）
The Agony of Opacity: Foundations for Reflective Interpretability in AI-Mediated Mental Health Support
University of California, San Francisco (UCSF)（加州大学旧金山分校）；Northwestern University（西北大学）；Feinberg School of Medicine, Northwestern University（西北大学费恩伯格医学院）；Microsoft Research（微软研究院）
Seeking Late Night Life Lines: Experiences of Conversational AI Use in Mental Health Crisis
University of Minnesota（明尼苏达大学）；Northwestern University（西北大学）；Dartmouth College（达特茅斯学院）；Microsoft（微软）；Mental Health America（心理健康美国）；Microsoft Research（微软研究院）
Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have developed a deep learning (DL) dialogue system called Serena. The system consists of a core generative model and post-processing algorithms. The core generative model is a 2.7 billion parameter Seq2Seq Transformer fine-tuned on thousands of transcripts of person-centered-therapy (PCT) sessions. The seri…
Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped. In this paper, we collaborate with one of the world's largest OMHC to develop an agent-based simulation framework and explore the trade-offs in different matching algorithms. The simulation framework allows us to compare current mechanisms and new algorithmic matching policies…
