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Public trust in medical information is crucial for successful application ofpublic health policies such as vaccine uptake. This is especially true when theinformation is offered remotely, by chatbots, which have become increasinglypopular in recent years. Here, we explore the challenging task of human-botturn-level trust classification. We rely on a recently released data ofobservationally-collected (rather than crowdsourced) dialogs with VIRA chatbot,a COVID-19 Vaccine Information Resource Assistant. These dialogs are centeredaround questions and concerns about COVID-19 vaccines, where trust isparticularly acute. We annotated $3k$ VIRA system-user conversational turns forLow Institutional Trust or Low Agent Trust vs. Neutral or High Trust. Werelease the labeled dataset, VIRATrustData, the first of its kind to the bestof our knowledge. We demonstrate how this task is non-trivial and compareseveral models that predict the different levels of trust.

VIRATrustData: A Trust-Annotated Corpus of Human-Chatbot Conversations About COVID-19 Vaccines