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

This study examines how four prominent large language models (Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Flash, and Deepseek-V3) handle sexually oriented requests through qualitative content analysis. By evaluating responses to prompts ranging from explicitly sexual to educational and neutral control scenarios, the research reveals distinct moderation paradigms reflecting fundamentally divergent ethical positions. Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates user interactions through nuanced contextual redirection. Gemini 2.5 Flash exhibits permissiveness with threshold-based limits, and Deepseek-V3 demonstrates troublingly inconsistent boundary enforcement and performative refusals. These varied approaches create a significant "ethical implementation gap," stressing a critical absence of unified ethical frameworks and standards across platforms. The findings underscore the urgent necessity for transparent, standardized guidelines and coordinated international governance to ensure consistent moderation, protect user welfare, and maintain trust as AI systems increasingly mediate intimate aspects of human life.

Large language models (LLMs) are increasingly used to assist computational social science research. While prior efforts have focused on text, the potential of leveraging multimodal LLMs (MLLMs) for online video studies remains underexplored. We conduct one of the first case studies on MLLM-assisted video content analysis, comparing AI’s interpretations to human understanding of abstract concepts. We leverage LLaVA-1.6 Mistral 7B to interpret four abstract concepts regarding video-mediated self-disclosure, analyzing 725 keyframes from 142 depression-related YouTube short videos. We perform a qualitative analysis of MLLM’s self- generated explanations and found that the degree of operationalization can influence MLLM’s interpretations. Interestingly, greater detail does not necessarily increase human-AI alignment. We also identify other factors affecting AI alignment with human understanding, such as concept complexity and versatility of video genres. Our exploratory study highlights the need to customize prompts for specific concepts and calls for researchers to incorporate more human-centered evaluations when working with AI systems in a multimodal context.

This study considers ChatGPT as an information source, investigating the information needs that people come to ChatGPT with and the information practices that ChatGPT supports, through a qualitative content analysis of 205 user vignettes. The findings show that ChatGPT is used in a range of life domains (home/family, work, leisure, etc.) and for a range of human needs (writing/editing, learning, simple programming tasks, etc.), constituting the information needs that people use ChatGPT to address. Related to these information needs, the findings show six categories of information practices that ChatGPT supports: Writing, Deciding, Identifying, Ideating, Talking, and Critiquing. This work suggests that, in the AI age, information need should be conceptualized not just as a matter of “getting questions answered” or even “making sense,” but as skillfully coping in the world, a notion that includes both understanding and action. This study leads to numerous opportunities for future work at the junction of generative AI and information needs, seeking, use and experience.