ABSTRACT:
Health misinformation has become an increasingly visible threat to public health, eroding public trust and influencing critical decisions such as vaccination and the acceptance of essential health services. In environments where fake news, information manipulation, and coordinated campaigns are amplified by digital platforms and algorithms, misleading perceptions consolidate, weakening the credibility of official messages and contributing to vaccine hesitancy. This paradox—greater information availability accompanied by declining trust—poses a structural challenge for public health policy. In this presentation, health-related “fake news” are defined as false or misleading content that mimics evidence, appears as personal testimony or denunciation, or combines factual elements with incorrect conclusions to generate fear, doubt, or rejection of health interventions. In the context of vaccination, these narratives typically operate through three mechanisms: perceived harm (attributing severe adverse effects to vaccines), moral or political suspicion (claims of experimentation, hidden interests, or government deception), and false alternatives (promoting remedies or practices as substitutes for vaccination). Despite their diversity, these narratives share a common effect: they transform uncertainty into distrust and shift vaccination decisions from a technical domain to an identity- and community-based one. The presentation will introduce data describing the circulation of these narratives and outline analytical hypotheses to examine how “truth” and credibility are constructed in community contexts, and how these processes influence doubt or rejection of public health interventions. It will also explore the origins of misinformation and its amplification through emotionally charged content. In urban marginal and rural communities, where information circulates through social media and interpersonal mediation, proximity and trust often outweigh official sources, allowing misinformation to compete not only with data, but with social relationships.