The rise of Generative AI (GenAI) has demonstrated significant potential to improve productivity and foster creativity among content creators, social media influencers with large audiences on platforms such as Instagram, TikTok, and YouTube. However, as GenAI tools became increasingly integrated into creative workflows, significant concerns have emerged about potential risks and harms, including misinformation, social biases, and threats to authenticity. While prior research in HCI and CSCW has documented the pressures content creators face within algorithmic ecosystems, relatively little is known about how creators practically manage responsibility work when using GenAI tools. To address this gap, we conducted semi-structured interviews (N = 16) with content creators active on popular social media platforms such as YouTube, Instagram, and TikTok, examining their motivations, practices, and specific challenges related to responsible GenAI use. Our findings reveal that creators’ motivations for practicing responsible AI use span personal reputation management, audience trust-building, and broader social responsibility. However, they face persistent tensions, as integrating GenAI significantly intensifies conflicts between responsible AI practices and the pressures of visibility, engagement, and monetization imposed by platform algorithms. Content creators are required to perform extensive and often invisible responsibility work, which directly conflicts with the rapid production cycles and engagement demands of algorithm-driven platforms. Based on these insights, we propose concrete socio-technical design implications at the individual, community, and institutional levels, advocating solutions that shift responsibility beyond individual creators alone.

Peer Reviewed

Humans continuously infer the states, goals, and behaviors of others by perceiving their surroundings in dynamic, real-world social interactions. However, most Theory of Mind (ToM) benchmarks only evaluate static, text-based scenarios, which have a significant gap compared to real interactions. We propose the SoMi-ToM benchmark, designed to evaluate multi-perspective ToM in embodied multi-agent complex social interactions. This benchmark is based on rich multimodal interaction data generated by the interaction environment SoMi, covering diverse crafting goals and social relationships. Our framework supports multi-level evaluation: (1) first-person evaluation provides multimodal (visual, dialogue, action, etc.) input from a first-person perspective during a task for real-time state inference, (2) third-person evaluation provides complete third-person perspective video and text records after a task for goal and behavior inference. This evaluation method allows for a more comprehensive examination of a model's ToM capabilities from both the subjective immediate experience and the objective global observation. We constructed a challenging dataset containing 35 third-person perspective videos, 363 first-person perspective images, and 1225 expert-annotated multiple-choice questions (three options). On this dataset, we systematically evaluated the performance of human subjects and several state-of-the-art large vision-language models (LVLMs). The results show that LVLMs perform significantly worse than humans on SoMi-ToM: the average accuracy gap between humans and models is 40.1% in first-person evaluation and 26.4% in third-person evaluation. This indicates that future LVLMs need to further improve their ToM capabilities in embodied, complex social interactions.

The rapid advancement of AI-generated content (AIGC) promises to transform various aspects of human life significantly. This work particularly focuses on the potential of AIGC to revolutionize image creation, such as photography and self-expression. We introduce ContextCam, a novel human-AI image co-creation system that integrates context awareness with mainstream AIGC technologies like Stable Diffusion. ContextCam provides user’s image creation process with inspiration by extracting relevant contextual data, and leverages Large Language Model-based (LLM) multi-agents to co-create images with the user. A study with 16 participants and 136 scenarios revealed that ContextCam was well-received, showcasing personalized and diverse outputs as well as interesting user behavior patterns. Participants provided positive feedback on their engagement and enjoyment when using ContextCam, and acknowledged its ability to inspire creativity.

VR live-streaming has become an emerging part on Douyin. This study aims to explore the technical modes, content strategies, user experiences in Douyin‘s VR live-streaming. Through interviews and focus groups, we found that VR technology is recognized by influencers and has become an essential part of their creative practice. For some influencers, VR technology is a key factor in enhancing audience engagement and immersive experiences, although technical literacy barriers may arise when setting up VR scenes. We also provide dimensions for improving and developing user adoption and experience of VR technology in social media environments.