💻 computer science

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

This study analyzes over 650,000 Reddit posts to reveal that while COVID-19 discussion topics on r/Belgium are driven by external events rather than internal social contagion, expressed sentiment exhibits significant homophily and is best modeled by the Smooth Latent-Expressed Bounded Confidence (SLEBC) framework, which distinguishes between noisy public expressions and underlying latent opinions.

Original authors: Tim Van Wesemael, Luis E. C. Rocha, Tijs W. Alleman, Jan M. Baetens

Published 2026-06-19

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Tim Van Wesemael, Luis E. C. Rocha, Tijs W. Alleman, Jan M. Baetens

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the Belgian Reddit community (r/Belgium) as a massive, bustling town square where thousands of people are shouting about three specific things during the pandemic: Lockdowns, Masks, and Vaccines.

This study is like a detective investigation into how people in this square talk to each other. The researchers wanted to know two main things:

Do people start talking about these topics because they heard a friend talking about them (social contagion)?
Do people change their tone (happy, angry, neutral) to match the person they are shouting at?

Here is what they found, explained simply:

1. The Topics Didn't "Catch" Like a Cold

You might think that if one person starts a thread about "Lockdowns," their friends would see it and start their own threads about it, creating a chain reaction.

The Finding: That didn't happen.
The Analogy: Imagine a town square where a loudspeaker (the government or news) suddenly announces a new rule. Everyone in the square suddenly starts shouting about that rule at the same time. But, if you look closely, no one is whispering to their neighbor, "Hey, let's start a new group chat about this!"
The study found that people only started new discussions about lockdowns, masks, or vaccines when external events happened (like a new law being passed or a news report). The topics didn't spread from person to person; they were "seeded" from the outside world.

2. The "Echo Chamber" Effect (Sentiment Homophily)

While people didn't start new topics by copying each other, they definitely changed how they felt when replying to someone.

The Finding: If you reply to an angry comment, your comment is likely to be angry too. If you reply to a happy one, you're likely to be happy.
The Analogy: Think of the conversation threads like a game of "Telephone," but instead of the message getting garbled, the mood gets amplified. If you walk up to a group of people who are already yelling in anger, you are likely to raise your voice to match them. The researchers called this homophily (loving the same). They found that people in the Reddit threads tended to "mirror" the emotional tone of the person they were directly replying to.

3. The "Two-Face" Theory (The SLEBC Model)

This is the most interesting part. The researchers built a computer model to understand why people mirror each other. They realized that people have two different "selves" in these conversations:

The Inner Self (Latent Sentiment): This is what you truly believe deep down. It changes slowly, like a glacier. It's built up over years of life experiences and long-term news.
The Outer Self (Expressed Sentiment): This is what you actually type into the comment box. It changes quickly, like a weather vane spinning in the wind.

The Finding: The study found that people's "Outer Self" (what they typed) was much more likely to snap to match the person they were replying to than their "Inner Self" was to change.
The Analogy: Imagine you are a calm, steady lighthouse (your Inner Self). But when a stormy ship (the parent comment) sails by and yells at you, you flash your light in a matching pattern just for a second (your Outer Self). Once the ship passes, you go back to your steady beam.
The researchers concluded that what people type in a comment is a noisy, unreliable signal of what they actually believe. They often just say what fits the immediate conversation to be polite or to fit in, even if it doesn't match their true feelings.

4. The "Bounded Confidence" Rule

The study tested different mathematical rules to see how people change their minds. They found that people don't just blindly agree with everyone (a linear rule). Instead, they only adjust their tone if the other person's opinion is "close enough" to their own.

The Analogy: Imagine you are wearing a pair of noise-canceling headphones. You can hear people talking to you, but if their voice is too different from yours (too far away in opinion), your headphones block them out, and you don't change your tune. You only listen and adjust if they are speaking in a similar frequency. This is called Bounded Confidence.

Summary of the "Detective Work"

Topics: Started by outside news, not by friends copying friends.
Feelings: People copy the mood of the person they are talking to directly.
Truth vs. Typing: What people type is often just a quick reaction to the person they are replying to, not a deep reflection of their true, long-held beliefs.
The Model: The best way to predict this behavior is to assume people have a "true self" that stays steady, but a "typing self" that quickly mimics the person they are talking to, but only if that person isn't too different from them.

The researchers suggest that if we want to understand public opinion during a crisis, we shouldn't just look at the "noise" of the comments (the Outer Self). We need to look for the steady "glacier" underneath (the Inner Self) to get the real picture.

Technical Summary: Social Contagion in COVID-19 Discussions within the Belgian Reddit Community

Problem Statement

Understanding the evolution of public sentiment toward COVID-19 mitigation measures on social media is critical for informing epidemiological models and public health policy. While social media serves as a vital data source for "infodemiology," existing research has largely focused on describing sentiment patterns rather than modeling the underlying dynamics of sentiment contagion and polarization. Specifically, there is a need to distinguish between topics driven by external information (e.g., policy announcements) versus those driven by internal social contagion, and to understand how expressed sentiment relates to underlying user opinions within threaded conversations. This study addresses these gaps by analyzing discussions on the Belgian Reddit community (r/Belgium) regarding lockdowns, masks, and vaccinations.

Methodology

Data Collection and Preprocessing

The study analyzed 655,642 posts (comments and submissions) from 28,559 unique users on r/Belgium between January 1, 2020, and June 30, 2022.

Topic Classification: Posts were classified into three mitigation topics (lockdowns, masks, vaccinations) using a multilingual BERT-based topic model (mbert-ctbt) trained specifically for the Belgian context.
Sentiment Analysis: English-language posts (approx. 75% of the dataset) were scored for sentiment using a RoBERTa-based classifier (roberta-tbsl). Sentiment was converted into a continuous value ( $s_k \in [-1, 1]$ ) by subtracting the negative score from the positive score.
Temporal Alignment: Posting volumes were correlated with external events (policy announcements, hospitalization counts, vaccination doses) using interrupted time series analysis.

Analytical Frameworks

Topic Contagion Testing: The authors tested whether prior participation in a topic increased the likelihood of a user initiating a new discussion on that same topic. This was evaluated by comparing the observed sequence of user initiations against a null model where initiations and participations were randomly interleaved.
Sentiment Homophily Quantification: The relationship between comment sentiment and parent sentiment was analyzed using 2D joint histograms. A homophily measure ( $h$ ) was calculated by weighting the deviation of observed sentiment pairs from a null distribution (randomly paired sentiments) based on their distance from the diagonal. Contexts included "ancestral" (recently read posts) and "user" (posts by the same author).
Mechanistic Modeling (SLEBC): A novel Smooth Latent-Expressed Bounded Confidence (SLEBC) model was developed to capture sentiment evolution.
- Latent State ( $u_i[t]$ ): An unobserved variable representing a user's underlying sentiment trajectory, updated stochastically based on interaction history.
- Expressed State ( $e_i[t]$ ): The observable sentiment of a specific comment, updated based on the user's latent state and the expressed sentiment of the immediate parent comment.
- Update Mechanism: The model employs a smooth bounded confidence kernel ( $B_{\alpha, \epsilon}$ ), where sentiment alignment occurs only if the difference between agents is below a threshold $\epsilon$ . This contrasts with linear update rules.
- Model Comparison: The SLEBC model was evaluated against two alternatives: a model with linear updates ( $L_\alpha$ ) and a stateless model ( $\bar{e}$ ) lacking a latent state. Model fit was assessed using the Watanabe-Akaike Information Criterion (WAIC).

Key Results

1. Topic Emergence and External Drivers

No Internal Social Contagion: Analysis of posting sequences revealed no evidence of social contagion in topic initiation. Users who participated in a discussion were not more likely to subsequently initiate a new discussion on the same topic.
External Seeding: Posting volume peaks for all three topics (lockdowns, masks, vaccinations) aligned strictly with external events, such as government policy announcements and media reports. Topic initiation appears to be seeded by external information rather than platform-internal spread.

2. Sentiment Homophily

Significant Homophily: Despite the lack of topic contagion, sentiment exhibited significant homophily. Comment sentiment correlated strongly with the sentiment of the parent post.
Context Dependence: Homophily was stronger in the "ancestral" context (immediate posting environment) than in the "user" context (history of the user's own interactions), suggesting users adapt to the immediate thread rather than selectively interacting with like-minded users over time.
Topic Differences: Lockdown discussions showed the highest polarization (distinct positive and negative clusters), while mask and vaccination discussions were predominantly negative with weaker homophily.

3. Model Performance and Dynamics

SLEBC Superiority: The SLEBC model provided the best fit (lowest WAIC) across all three topics compared to the linear and stateless alternatives.
Expressed vs. Latent Adaptation: The model inferred that expressed sentiment adapts more strongly to the immediate parent comment than the user's latent state updates based on interaction history.
- Proportions: For 74% of lockdown users, 70% of mask users, and 51% of vaccination users, the adaptation strength to the parent ( $\alpha_e$ ) exceeded the latent update strength ( $\alpha_u$ ).
Bounded Confidence: The bounded confidence mechanism was essential; the linear model substantially underestimated homophily, while the stateless model failed to capture the sentiment distribution over time.

Significance and Claims

The paper claims that infodemic models for Reddit-like platforms should be structured differently than traditional contagion models:

Topic Seeding: Models should treat topic emergence as driven by external information sources (official communications, media) rather than assuming endogenous social contagion.
Sentiment Mechanisms: Sentiment spread is better modeled via bounded confidence mechanisms (where influence is limited by opinion similarity) rather than linear contagion.
Latent vs. Expressed: There is a critical distinction between a user's underlying latent sentiment and their expressed sentiment. Expressed sentiment is a "noisy" signal that adapts rapidly to immediate conversational context (the parent post) and may poorly reflect a user's true underlying opinion.
Topic Specificity: Mitigation measures (lockdowns, masks, vaccines) exhibit distinct temporal and sentiment patterns and should not be aggregated into a single "COVID-19 sentiment" variable.

The study concludes that while social media discussions are not driven by internal topic contagion, the dynamics of sentiment within threads are shaped by local interactions and bounded confidence, necessitating models that incorporate external drivers for volume and bounded confidence mechanisms for sentiment evolution.