Modeling Vaccine Discourse and Polarization on YouTube in Brazil: A Semi-Supervised Stance Detection Approach - Oliveira, Geovana S. de; Ferreira, Carlos H. G. (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Vaccination is a cornerstone of public health, yet the COVID-19 pandemic exposed how online misinformation and polarization can undermine immunization efforts. Studying these dynamics at scale is challenging due to scarce annotated data, class imbalance, and high labeling costs. This work presents a longitudinal analysis of vaccine discourse on YouTube in Brazil (2018–2024), using a semi-supervised stance detection framework to classify 1.4 million comments. The approach enables effective learning from limited supervision and produces a publicly available Portuguese stance classification model alongside a large-scale analysis of online debate. Results show strong performance despite class imbalance and enable the analysis of polarization over time. We find that polarization intensified during the pandemic and became more fragmented afterward, with science communication and digital-native channels concentrating both supportive and opposing engagement.