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

TL;DR

This longitudinal study explores how users develop and negotiate trust over time in social LLM-based chatbots, specifically Snapchat's My AI, highlighting factors influencing trust dynamics.

Contribution

It introduces a conceptual model of trust as a dynamic, evolving user state shaped by interaction and user expectations in social chatbot contexts.

Findings

Trust evolves through interaction and user adaptation.

Excessive anthropomorphism can undermine trust.

Transparency and conversational ability influence trust stability.

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.