Social Contagion in COVID-19 Discussions Within the Belgian Reddit Community: Statistical and Modeling Study.
Understanding how sentiment toward COVID-19 mitigation measures evolves on social networks can help to inform infectious disease models and policymakers. Even though numerous studies have described social media interactions during the pandemic, few have modeled the underlying dynamics of sentiment contagion and polarization.
This study aimed to investigate topic emergence and sentiment evolution in COVID-19 mitigation discussions on r/Belgium, focusing on (1) whether discussion topics exhibited social contagion, (2) whether expressed sentiment displayed homophily, and (3) how this homophily formation can be captured by a mechanistic model.
We classified posts created on r/Belgium between January 1, 2020, and June 30, 2022, into lockdowns, masks, and vaccination, using a pretrained bidirectional encoder representations from transformers (BERT) topic model, and assigned English posts a sentiment, using a robustly optimized BERT pretraining approach (RoBERTa)-based sentiment classifier. We then examined temporal patterns of post volume and tested for social contagion in topic initiation. Sentiment homophily was quantified by comparing observed comment-parent sentiment pairs to null distributions. The novel smooth latent-expressed bounded confidence (SLEBC) model dynamically captured sentiment evolution, distinguishing between latent sentiment trajectories and noisy expressed sentiment. We tested the model against 2 alternatives, one with a linear update and one without the latent state, using the Watanabe-Akaike information criterion.
Analysis of 655,642 posts made by 28,559 users revealed that post volume was associated with external events such as policy announcements and media reports. There was no evidence of within-Reddit social contagion in topic initiation. However, sentiment exhibited significant homophily, with comment sentiment correlating with parent comment sentiment. The SLEBC model reproduced observed sentiment patterns (Watanabe-Akaike information criterion: -28.5 to -18.4 across topics), outperforming both alternatives (-21.1 to -17.4 for the linear model and 6.8 to 692 for the one without the latent state). It slightly underestimated sentiment homophily but still outperformed the alternatives in this regard. In the SLEBC model, expressed sentiment adapts more strongly to the immediate parent comment than the user's latent state updates based on their interaction history (proportions of users showing this pattern: 0.74, 0.70, and 0.51 for lockdowns, masks, and vaccination).
Discussion topics on r/Belgium are not associated with social contagion within the platform, but sentiment dynamics are shaped by within-thread interactions. The SLEBC model suggests that users adapt their expressed sentiment to match the post they reply to, highlighting that expressed sentiment may poorly reflect underlying latent sentiment. Infodemic models for Reddit-like platforms could benefit from incorporating external information sources for topic seeding and from using bounded confidence rather than linear contagion mechanisms for sentiment spread.
This study aimed to investigate topic emergence and sentiment evolution in COVID-19 mitigation discussions on r/Belgium, focusing on (1) whether discussion topics exhibited social contagion, (2) whether expressed sentiment displayed homophily, and (3) how this homophily formation can be captured by a mechanistic model.
We classified posts created on r/Belgium between January 1, 2020, and June 30, 2022, into lockdowns, masks, and vaccination, using a pretrained bidirectional encoder representations from transformers (BERT) topic model, and assigned English posts a sentiment, using a robustly optimized BERT pretraining approach (RoBERTa)-based sentiment classifier. We then examined temporal patterns of post volume and tested for social contagion in topic initiation. Sentiment homophily was quantified by comparing observed comment-parent sentiment pairs to null distributions. The novel smooth latent-expressed bounded confidence (SLEBC) model dynamically captured sentiment evolution, distinguishing between latent sentiment trajectories and noisy expressed sentiment. We tested the model against 2 alternatives, one with a linear update and one without the latent state, using the Watanabe-Akaike information criterion.
Analysis of 655,642 posts made by 28,559 users revealed that post volume was associated with external events such as policy announcements and media reports. There was no evidence of within-Reddit social contagion in topic initiation. However, sentiment exhibited significant homophily, with comment sentiment correlating with parent comment sentiment. The SLEBC model reproduced observed sentiment patterns (Watanabe-Akaike information criterion: -28.5 to -18.4 across topics), outperforming both alternatives (-21.1 to -17.4 for the linear model and 6.8 to 692 for the one without the latent state). It slightly underestimated sentiment homophily but still outperformed the alternatives in this regard. In the SLEBC model, expressed sentiment adapts more strongly to the immediate parent comment than the user's latent state updates based on their interaction history (proportions of users showing this pattern: 0.74, 0.70, and 0.51 for lockdowns, masks, and vaccination).
Discussion topics on r/Belgium are not associated with social contagion within the platform, but sentiment dynamics are shaped by within-thread interactions. The SLEBC model suggests that users adapt their expressed sentiment to match the post they reply to, highlighting that expressed sentiment may poorly reflect underlying latent sentiment. Infodemic models for Reddit-like platforms could benefit from incorporating external information sources for topic seeding and from using bounded confidence rather than linear contagion mechanisms for sentiment spread.