Social Contagion in COVID-19 Discussions within the Belgian Reddit Community: A Statistical and Modeling Study

Understanding how sentiment toward COVID-19 mitigation measures evolves on social media can inform both epidemiological models and public health policy. We analyzed 655,642 posts by 28,559 users on r/Belgium from January 2020 to June 2022, classifying posts into three mitigation topics (lockdowns, masks, vaccinations) using a BERT-based topic model and scoring sentiment with a RoBERTa-based classifier. Post volume tracked external events such as policy announcements, but we found no evidence of within-Reddit social contagion in topic initiation, suggesting topics are seeded by external information rather than platform-internal spread. Sentiment, however, exhibited significant homophily: comment sentiment correlated with that of the parent post. To capture the underlying dynamics, we developed the Smooth Latent-Expressed Bounded Confidence (SLEBC) model, which distinguishes a latent sentiment trajectory from noisy expressed sentiment and uses bounded confidence rather than linear update rules. Evaluated against two alternatives by WAIC, SLEBC fit best across all three topics. The model indicates that expressed sentiment adapts more strongly to the immediate parent comment than the user's latent state updates from interaction history, suggesting that expressed sentiment is a poor proxy for underlying opinion. These findings imply that infodemic models for Reddit-like platforms should seed topics from external sources and model sentiment spread via bounded confidence mechanisms.