Geovana S. de Oliveira

arXiv2026

The Brazilian Vaccination Debate on YouTube: Topics, Perspectives, and Engagement Dynamics

Vaccination debates are central to online public health communication, as COVID-19 intensified disputes over scientific authority, institutional trust, and political identity. Yet studies often isolate semantic structure, stance, misinformation, and engagement, leaving their interplay over time poorly understood. We conduct a multilevel computational text analysis based on language models applied to 1.27 million Brazilian YouTube comments from 2018 to 2024, using what is, to our knowledge, the largest dataset of Brazilian vaccine discourse on the Web. We contrast producer framing in titles with audience discourse in comments, integrating Topic-derived themes with engagement metadata, conversational timing, stance-derived vaccine positions, and pre-pandemic, pandemic, and post-pandemic periods. Results show that COVID-19 dominates biomedical and informational themes in titles, whereas comments span personal health reports, vaccine effects, information credibility, conspiracy narratives, and political disputes. Health-related macro-topics dominate in scale and persistence, while conspiratorial and political themes are associated with faster interactions and a greater concentration of vaccine-opposing engagement. Post-pandemic activity remains centered on health experiences, vaccine effects, and information credibility, indicating no return to the pre-pandemic thematic configuration. By integrating semantic, interactional, stance, and temporal dimensions, this study shows how audiences reframe producer-framed health content and how vaccine controversies persist beyond the acute pandemic period.

cs.SI↗

arXiv2026

Who Shapes Brazil's Vaccine Debate? Semi-Supervised Modeling of Stance and Polarization in YouTube's Media Ecosystem

Vaccination remains a cornerstone of global public health, yet the COVID-19 pandemic exposed how online misinformation, political polarization, and declining institutional trust can undermine immunization efforts. Most of the prior computational studies that analyzed vaccine discourse on social platforms focus on English-language data, specific vaccines, or short time windows, impairing our understanding of long-term dynamics in high-impact, non-English contexts like Brazil, home to one of the world's most comprehensive immunization systems. We here present the largest longitudinal study of Brazil's vaccine discourse on YouTube, leveraging a semi-supervised stance detection framework that combines self-labeling and self-training to classify nearly 1.4 million comments. By integrating stance with temporal patterns, engagement metrics, and channel taxonomy (legacy media, science communicators, digital-native outlets), we map how pro- and anti-vaccine narratives evolve and circulate within a hybrid media ecosystem. Our results show that semi-supervised learning substantially improves stance classification robustness, enabling fine-grained tracking of public attitudes across Brazil's full immunization schedule. Polarization spikes during epidemiological crises, especially COVID-19, but becomes fragmented across vaccines and interaction patterns in the post-pandemic period. Notably, science communication and digital-native channels emerge as the primary loci of both supportive and oppositional engagement, revealing structural vulnerabilities in contemporary health communication. Thus, our work advances computational methods for large-scale stance modeling while offering actionable evidence for public health agencies, platform governance, and online information ecosystems.

cs.CY↗