ArticleBMJ digital health & AI2026
What can chatbot conversations reveal about vaccine concerns? An observational topic modelling study for public health infoveillance.
Article in BMJ digital health & AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: The COVID-19 pandemic was marked by a surge of online information, including misinformation about vaccines. Health agencies recommend infoveillance to track public attitudes on immunisation, particularly during health emergencies. We sought to investigate how chatbots may serve as a novel data source for digital monitoring of vaccine concerns. Chatbots are an increasingly popular two-way health communication tool, and an analysis of anonymised chatbot inputs could identify emerging misinformation and public concerns. Methods and analysis: We used a topic modeller and large language model (LLM)-based few-shot learner to understand the themes and emotional tone of chats users sent to the Vaccine Information Resource Assistant (VIRA), a non-generative chatbot created by Johns Hopkins to answer questions about COVID-19 vaccines that was used or shared by health departments in 12 US states. We employed BERTopic to conduct topic modelling on user textual data and conversations. We also used OpenAI's LLM, GPT-4o, employing a human-labelled set of chats and instructions to help fine-tune the model and to classify a random subset of 8760 chats sent to VIRA into pro-vaccine, neutral and anti-vaccine. Results: Analysing 30 336 chats users sent to VIRA over a 2-year period, in English and Spanish, we found most focused on vaccine recommendations and safety (71%), with far fewer chats addressing conspiracies and questioning the need for vaccines (3%). Sentiment analysis of a randomly selected subset of chats (n=8760) indicated that English-language chats were more likely than Spanish-language chats to express negative emotions about vaccines (12% vs 4%), but the majority of messages sent by users in either language were classified as neutral. Conclusion: This observational analysis offers a detailed case study on how anonymised chatbot dialogues may expand on insights from social media for health agencies and others seeking to monitor opinions and work to support strong vaccine confidence. As chatbots become increasingly fluid conversational tools, the findings suggest significant potential for chatbot-facilitated health interventions, both for public engagement as well as understanding.
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