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

BACKGROUND Understanding how attitudes toward COVID-19 mitigation measures spread on social networks is crucial to inform infectious disease modelers and policymakers. Even though previous studies have described social media interactions during the pandemic, there remains potential to model the underlying dynamics of sentiment contagion and polarization. OBJECTIVE This study investigated the emergence and evolution of discussions on COVID-19 mitigation measures within the Belgian Reddit community (r/Belgium), focusing on how sentiments diffused among users over time. Concretely, it examined whether topic discussions exhibited patterns of social contagion and how expressed sentiments were shaped by prior interactions, contributing to homophily and polarization. METHODS We analyzed posts created on r/Belgium between 1 January 2020 and 30 June 2022. Posts were classified into three mitigation topics, lockdowns, masks, and vaccination, using a BERT-based topic model. Sentiment was assigned to English posts using a RoBERTa-based sentiment classifier. We examined temporal patterns of post volume and tested for social contagion in topic initiation using null models. Sentiment homophily was quantified by comparing observed comment-parent sentiment pairs to null distributions. We developed the Smooth Internal Expressed Bounded Confidence (SIEBC) model and tested it against two alternatives, to add mechanistic intuition to the observed homophily. RESULTS The analysis of 655,642 posts made by 28559 users revealed that post volume was strongly associated with external events such as policy announcements and media reports, but not with within-Reddit interactions. There was no evidence of social contagion in topic initiation. However, sentiment exhibited significant homophily, with comment sentiment correlating with parent comment sentiment. The SIEBC model reproduced observed sentiment patterns, with Kolmogorov Smirnov statistic between predicted and observed sentiment distributions ranging from 0.043 to 0.067. It slightly underestimated homophily, but still outperformed alternative models. The model revealed that expressed sentiment adapts more strongly to parent comments than internal sentiment adapts to other interactions (proportion of users showing this pattern: 0.75, 0.70, and 0.53 for lockdowns, masks, and vaccination). CONCLUSIONS Topic discussions on r/Belgium are driven primarily by external events rather than social contagion within the platform. In contrast, for sentiment there is observed homophily. This can be explained by users adapting their expressed sentiment to match the conversational context of threads. The SIEBC model demonstrates that expressed sentiment may not reflect users’ internal attitudes, highlighting the importance of handling the former with care. These findings suggest that epidemic-social models would benefit from incorporating external information sources for topic dynamics and using complex mechanisms, such as a bounded confidence kernel, for sentiment spread.