Filter by author: Bianca Nowak

AI-based chatbots offer new opportunities for communicating science-based information, but often fall short of established standards. We conducted two pre-registered experiments examining user perceptions of an AI-based chatbot providing information on nanoparticles in sunscreen. Study one (N = 508) tested whether a disclaimer about the chatbot's uncertain training data affected perceived source trustworthiness and information credibility. The results showed no significant effect of the disclaimer; perceptions were primarily influenced by users' prior attitudes. Study two (N = 1059) tested the evaluation of information on nanoparticles in sunscreen in an experiment with a 2 (source: scientist vs. AI-based chatbot) ×2 (presentation: static vs. dynamic) between-subjects design. The study showed that the scientist was evaluated as more trustworthy and the provided information seen as more credible compared to the AI-based chatbot. The two studies highlight the relevance of perceived objectivity in science and health communication, whether executed by humans or machines.

Scientists and experts using social media platforms to engage with the public risk negative public feedback, potentially harming their efforts. This paper addresses how negative user comments affect experts’ trustworthiness and the messages’ credibility depending on whether they frame their message as scientific versus anecdotal using an online study with a 2 (evidence type: scientific vs. anecdotal) x 3 (comments: neutral, negative-factual, negative-emotional) between-subjects design. The results suggest that relying on scientific evidence when engaging in emotionally charged discourses is beneficial. Negative-emotional comments have a significant negative impact on trustworthiness, which is especially pronounced when using anecdotal evidence.