Trust as a Situated User State in Social LLM-Based Chatbots: A Longitudinal Study of Snapchat's My AI

2026-04-24

AI Summary

This study investigates how user trust dynamically evolves over time during interactions with a social large language model chatbot (Snapchat My AI). Through a four-week longitudinal qualitative study (N=27) combining in-depth interviews and behavioral observation, the research demonstrates that trust is not a one-time judgment but an ongoing, negotiated process shaped by multiple factors—including perceived competence, conversational fluency, degree of anthropomorphism, transparency, privacy concerns, and platform-level trust. The findings reveal that excessive anthropomorphism and insufficient transparency can undermine long-term trust, whereas natural and fluent dialogue enhances user engagement. Building on these insights, the study proposes a dynamic trust model centered on interaction context and user expectations, offering a theoretical foundation for designing human-centered, adaptive conversational systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: User Experience and Usability
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• trust
• social chatbots
• large language models
• longitudinal study
• user state

Innovation

• trust dynamics
• longitudinal study
• social chatbots
• human-AI interaction
• LLM-based agents