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

Area of Science:

• Social Network Analysis
• Computational Social Science
• Epidemiology

Background:

• Understanding public sentiment on COVID-19 mitigation is crucial for policy and infectious disease modeling.
• Previous studies have described social media interactions but lacked models for sentiment contagion and polarization dynamics.
• Few studies have modeled the underlying dynamics of sentiment contagion and polarization on social networks during the pandemic.

Purpose of the Study:

• Investigate topic emergence and sentiment evolution in COVID-19 mitigation discussions on r/Belgium.
• Determine if discussion topics exhibit social contagion and if expressed sentiment shows homophily.
• Develop and test a mechanistic model to capture sentiment homophily formation.

Main Methods:

• Classified 655,642 posts (Jan 2020-Jun 2022) on r/Belgium into lockdowns, masks, and vaccination using BERT topic modeling.
• Assigned sentiment using a RoBERTa-based classifier and examined temporal patterns and topic contagion.
• Quantified sentiment homophily and modeled sentiment evolution using the novel smooth latent-expressed bounded confidence (SLEBC) model, comparing it to alternatives.

Main Results:

• Post volume correlated with external events; no evidence of within-Reddit topic contagion was found.
• Sentiment exhibited significant homophily, with comment sentiment correlating strongly with parent comment sentiment.
• The SLEBC model accurately reproduced observed sentiment patterns, outperforming linear and latent-state-free models.

Conclusions:

• COVID-19 discussion topics on r/Belgium do not spread via social contagion, but sentiment dynamics are influenced by within-thread interactions.
• Expressed sentiment on the platform may not accurately reflect users' latent sentiments, as users tend to match replies to parent comments.
• Infodemic models for Reddit-like platforms should incorporate external information for topic seeding and use bounded confidence mechanisms for sentiment spread.