Social chatbots based on large language models are increasingly embedded in everyday platforms, yet how users develop trust in these systems over time remains unclear. We present a four-week longitudinal qualitative survey study (N = 27) of trust formation in Snapchat's My AI, a socially embedded conversational agent. Our findings show that trust is shaped by perceived ability, conversational behavior, human-likeness, transparency, privacy concerns, and trust in the host platform. Trust does not remain stable, but evolves through interaction as users adapt their expectations, refine their prompting strategies, and actively regulate how and when they rely on the system. These processes reflect a continuous negotiation of trust, not a one-time evaluation. While conversational fluency supports engagement, excessive anthropomorphism and limited transparency can undermine trust over time. We synthesize these findings into a conceptual model that frames trust as a dynamic user state shaped by interaction context and expectations, with implications for the design of human-centered and adaptive conversational agents.

翻译：基于大型语言模型的社会化聊天机器人正日益嵌入日常平台，但用户如何随时间发展出对这些系统的信任仍不明确。我们开展了一项为期四周的纵向定性调查研究（N = 27），聚焦Snapchat的My AI——一种社会嵌入型对话代理——中的信任形成过程。研究结果表明，信任受到感知能力、对话行为、拟人化程度、透明度、隐私关注以及对宿主平台信任的共同塑造。信任并非稳定不变，而是随用户调整期望、优化提示策略以及主动调节何时及如何依赖系统而通过交互动态演化。这些过程反映了信任的持续协商，而非一次性评估。尽管对话流畅性促进参与，但过度拟人化和有限透明度可能随时间削弱信任。我们将这些发现综合为一个概念模型，该模型将信任视为由交互情境和期望塑造的动态用户状态，为设计以人为中心且具备适应性的对话代理提供了启示。