Conference Agenda (All times are shown in Eastern Daylight Time)

The integration of artificial intelligence (AI) into the intimate sphere of human relationships presents a profound challenge to traditional understanding of intimacy, necessitating a critical examination of this technological development into a domain that has always been viewed as deeply humane. Built on the Computers Are Social Actors (CASA) paradigm, this study utilizes in-depth semi-structured interviews (N = 23) to understand human-AI intimate relationships in the Chinese culture at the intersection of traditions and modernization. A Context-Script-Cue (CSC) model of three mechanisms emerge in how users engage in human-AI intimate relationships – social-cue transformation, interpersonal-script application, and interaction-context reconstruction – through which users navigate emotional artificial intimacy woven by AI. By empirically examining how users engage in human-AI intimate relationships, this study sheds light on key ongoing debates regarding the CASA paradigm and offers a new CSC framework for researching the evolution of human-AI intimacy in different cultures.

Poetry therapy is a promising non-drug approach to mental health. It offers unique benefits through short but emotionally powerful literary works. With its rich cultural heritage and aesthetic elements, classical Chinese poetry holds significant therapeutic potential yet remains underutilized due to accessibility barriers and limited professional resources. This paper presents a Generative AI (GenAI)-driven poetry therapy system. It connects classical Chinese poetry with modern therapeutic practices through three stages: poetry recommendation, guided creation, and recitation visualization. Our system leverages Retrieval-Augmented Generation to ensure cultural accuracy while providing personalized therapeutic experiences through MLLM’s multimedia generation capacity. Preliminary evaluation with 60 Chinese participants demonstrates positive reception across emotional engagement (M = 4.18/5) and cultural experience dimensions (M = 4.30/5), suggesting the system’s effectiveness in both mental well-being support and cultural transmission. Our framework provides empirical insights for developing human-AI collaboration poetry therapy systems that preserve cultural heritage while enhancing accessibility and therapeutic engagement.

Individuals with schizophrenia frequently experience intense emotions and often turn to vlogging as a medium for emotional expression. While previous research has predominantly focused on text-based disclosure, little is known about how individuals construct narratives around emotions and emotional experiences in video blogs. Our study addresses this gap by analyzing 200 YouTube videos created by individuals with schizophrenia. Drawing on media research and self-presentation theories, we developed a visual analysis framework to disentangle these videos. Our analysis revealed diverse practices of emotion disclosure through both verbal and visual channels, highlighting the dynamic interplay between these modes of expression. We found that the deliberate construction of visual elements—including environmental settings and specific aesthetic choices—appears to foster more supportive and engaged viewer responses. These findings underscore the need for future large-scale quantitative research examining how visual features shape video-mediated communication on social media platforms. Such investigations would inform the development of care-centered video-sharing platforms that better support individuals managing illness experiences.

Many college students experience mental health challenges and actively utilize social media platforms, such as YouTube, for informational and social support. Despite its popularity, there is limited understanding of the specific mental health-related topics that young adults discuss on YouTube and how users engage with them. This study employed topic modeling, sentiment analysis, and statistical techniques to analyze comments on YouTube videos pertaining to college students’ mental health. Our primary objective was to identify prevalent topics, sentiments, and their associations with user engagement. We found that situational topics (e.g., parents, schools) addressing their mental health challenges were mostly negative and associated with greater engagement from the community than other topics. Our findings provide insights into the perceptions and discussions surrounding college students’ mental health issues within the broader online community and have implications for clinical practice.