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REVIEW 5 major objections 5 minor 90 references

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

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read COVID-19 mitigation topics on r/belgium are seeded externally, not through within-Reddit contagion, while sentiment does track the parent post.

desk verdict Useful empirical study with a clever latent-state sentiment model, but the headline 'no topic contagion' claim outruns the test that produced it. read the letter →

arxiv 2505.20185 v2 pith:6FPUQLOP submitted 2025-05-26 cs.SI physics.soc-ph

Tim Van Wesemael , Luis E. C. Rocha , Tijs W. Alleman , Jan M. Baetens This is my paper · ORCID
classification cs.SIphysics.soc-ph MSC 91D3062P2568T50
keywords COVID-19mitigationRedditsentimentanalysistopicmodellingsocialcontagionhomophilyboundedconfidencemodelinfodemic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper studies 655,642 posts by 28,559 users in r/belgium from January 2020 to June 2022 and asks whether discussions of lockdowns, masks, and vaccination spread inside the community or are driven from outside. It concludes that topic initiation shows no social contagion: having participated in a discussion on one of these mitigation measures does not make a user more likely to start a new one, and post volume aligns with external announcements rather than with hospital or vaccination data. Sentiment behaves differently. Comments tend to match the sentiment of their parent post, producing homophily for all three topics, with lockdown discussions the most polarised. To explain this, the authors fit the Smooth Internal Expressed Bounded Confidence (SIEBC) model, which separates a latent internal sentiment from the expressed sentiment in each comment; the fit suggests expressed sentiment adapts strongly to the immediate parent while the internal state changes only slowly. If these findings hold, coupled epidemic-social models should seed discussion topics from outside the platform and model sentiment spread through bounded-confidence adaptation rather than treating Reddit sentiment as a direct opinion poll.

What carries the argument

The load-bearing object is the Smooth Internal Expressed Bounded Confidence (SIEBC) model, built around the smooth bounded-confidence kernel $B_{\alpha,\epsilon}(s_1,s_2) = s_1 + \alpha(s_2-s_1)/(1+\exp(\gamma((s_2-s_1)^2-\epsilon^2)))$, where $\alpha$ is the update strength, $\epsilon$ the opinion-difference threshold, and $\gamma=50$ sets the sharpness of the threshold. Each user has a latent internal sentiment $u_i[t]$ and an observable expressed sentiment $e_i[t]$; the expressed state is drawn from a truncated normal around the kernel applied to the internal state and the parent comment's sentiment, while the internal state evolves under the same kernel applied to all interactions received since the previous comment. The topic-contagion test uses a combinatoric null model over each user's sequence of initiations and participations, and the sentiment tests use a null model that randomly reconnects comment-parent sentiment pairs, summarised by the homophily measure $h$. The model is fitted per topic by MCMC, and its predictions are compared with observations using the Wasserstein metric, with WAIC used to compare against alternative specifications.

What would settle it

Human-annotate a random sample of initiating and participating posts and re-run the null-model statistic with corrected topic labels; if the corrected initiations cluster after participations rather than before them, the claim that discussion topics are not contagious within r/belgium would be refuted.

Watch

Extended reading notes

Core claim

The central discovery is an asymmetry between what spreads and what does not. On r/belgium, the volume of posts about lockdowns, masks, and vaccination tracks external events such as policy announcements, and a user's history of participating in a topic does not predict initiation of a new discussion on that topic; by a combinatoric null model, initiations occur earlier in a user's sequence than expected, not later. Sentiment, however, is contagious in a local sense: comment sentiment correlates with parent sentiment, homophily measures reach 0.228 (lockdowns), 0.198 (masks), and 0.133 (vaccination), and the effect is stronger for the thread ancestors a user reads than for the comments the user has previously written. Fitting the two-state SIEBC model, the paper finds that for most users the expressed sentiment adapts more to the parent comment than the latent state adapts to interaction history, so expressed sentiment is a noisy proxy for underlying opinion. The paper therefore proposes that infodemic models separate external topic seeding from internal sentiment adaptation.

Load-bearing premise

All downstream conclusions inherit whatever errors the automated classifiers make: the topic model must correctly assign posts to lockdown, mask, or vaccination topics in four languages, the English-only RoBERTa sentiment model must measure sentiment on Reddit well, and the no-contagion test assumes the within-user ordering null model is a meaningful test even though it never tracks what a user actually saw.

Editorial extensions

If this is right

  • Coupled epidemic-social models should not let topics arise spontaneously inside the simulated platform; topic popularity should be seeded by external information such as government announcements and media coverage.
  • Sentiment should be modeled with a bounded-confidence mechanism rather than a linear update rule, because the bounded SIEBC kernel reproduces the observed homophily while the linear kernel does not.
  • Expressed sentiment on Reddit should not be treated as a direct measure of underlying user opinion, since the parent comment's sentiment is a major influence on the reply's expressed sentiment.
  • The three mitigation topics should be followed separately in epidemic-social models, because lockdowns show the most volume and polarisation while vaccination shows the least homophily.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the external-seeding result generalizes, then measuring an infodemic from social media alone conflates two distinct processes: news-driven topic attention and platform-internal sentiment adaptation; a testable consequence is that topic initiation rates should track news or policy event intensity in other subreddits and countries.
  • Because sentiment was computed only on English posts, about 75% of the data, the paper leaves open whether parent-comment adaptation is identical in Dutch, French, and German threads; a replication with per-language sentiment classifiers would test the symmetry of the mechanism.
  • Because the internal sentiment estimates have no ground truth, a natural next test is to compare SIEBC-reconstructed internal states with later observable behaviour, such as leaving the subreddit, switching topics, or survey-reported attitudes.
  • If expressed sentiment is a poor proxy for underlying opinion, public-health sentiment dashboards should be recalibrated or paired with latent-state models rather than read as opinion polls.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This manuscript studies 655,642 posts from the Belgian r/belgium subreddit between January 2020 and June 2022. Posts are classified into three mitigation topics (lockdowns, masks, vaccination) using a multilingual BERT model, and English-language posts are scored with a RoBERTa sentiment classifier. The authors report that topic volume tracks external events and that, based on a within-user ordering null model, users are not more likely to initiate a topic after having participated in it, leading to the conclusion that there is no social contagion in topic initiation. They further find sentiment homophily between comments and their parents, and introduce a Smooth Internal/Latent Expressed Bounded Confidence (SIEBC/SLEBC) model that distinguishes a latent internal sentiment from expressed sentiment, fitting it with MCMC. The abstract claims the model was evaluated against two alternatives by WAIC, but the full text reports only Wasserstein metric comparisons with a single linear kernel.

Significance. If fully supported, the distinction between externally driven topic adoption and sentiment-driven homophily would be a useful empirical input for infodemic and coupled epidemic-social models, and the bounded-confidence latent-state model is a plausible mechanistic contribution. The paper benefits from a large curated dataset, reproducible code, and a clear research question. However, the central negative claim about topic contagion is not supported by the analysis actually performed, and the model evaluation is partly in-sample and overclaimed in the abstract. These issues substantially reduce the confidence in the stated conclusions, though the descriptive components and the model itself retain value if reframed with appropriately bounded claims.

major comments (5)
  1. [6.2] Section 6.2, Eqs. (3)-(5): The null model randomizes the order of each user's own initiations and participations, so it can only detect whether a user's earlier participations in the same topic make that user's later initiations more likely. It does not measure whether users are exposed to same-topic posts written by other users before initiating, which is the standard meaning of social contagion. In particular, a user may read threads without commenting or may be influenced by another user's same-topic submission or comment, and the null model is blind to both mechanisms. The observed result that initiations tend to occur early in a user's sequence is therefore not evidence against cross-user contagion; it is merely evidence against a particular form of self-priming. The conclusion in Section 6.2 that 'there is no social contagion of discussions on the three mitigation topics within Reddit's r/belgium community, neither simple, nor complex' is an overstatement of what the test can show.
  2. [Abstract / Section 6.4] The abstract states that the model was 'Evaluated against two alternatives by WAIC, SLEBC fit best across all three topics.' However, the full text does not mention WAIC or a second alternative; Section 6.4 reports only Wasserstein metrics W1 for SIEBC versus a single linear kernel (Eq. (10)). Either the abstract describes an evaluation that is not reported in the body, or the body is missing the WAIC analysis. This mismatch must be resolved, and the model name should be unified (SLEBC in the abstract, SIEBC elsewhere).
  3. [6.4, 7.3] The SIEBC model is fitted to the expressed sentiments of the same users on which the homophily statistic h is later computed, so the statement that the model 'predicts' the observed homophily (also in Section 7.3) is an in-sample comparison rather than a predictive test. The absolute fit is weak (W1 between 0.493 and 0.607), the predicted sentiment distributions do not reproduce the neutral peak (Figure 8A-C), and for vaccination the predicted homophily differs markedly from the observed value. The authors should either provide an out-of-sample or cross-validated evaluation or explicitly reframe the homophily agreement as a descriptive fit.
  4. [4, 6.3, 6.4] Sentiment scores are assigned only to English-language posts (about 75% of the data); the sentiment homophily analysis (Section 6.3) and the SIEBC fit (Section 6.4) are therefore restricted to that subset. The results and conclusions, however, are phrased as statements about 'the r/belgium community' without this caveat, and Section 7.6 does not list this restriction among the limitations. The paper should state this limitation explicitly and discuss potential language-related biases in homophily and model parameters.
  5. [7.3, 7.6] The conclusion that 'expressed sentiment is a poor proxy for underlying opinion' depends on the latent internal state u_i[t] being a valid construct, yet Section 7.6 acknowledges that 'the obtained values for the internal state are not compared to a ground truth.' Without external validation, the interpretation of the latent state as 'actual sentiment' is speculative. The authors should either temper the claim or provide some form of validation, such as testing against user-level outcomes or known temporal events.
minor comments (5)
  1. [5.1] The threshold for significantly negative days (0.275 quantile and at least 50 comments) is chosen 'such that one to five negative days can be identified for each topic'; this data-dependent selection should be disclosed as exploratory rather than confirmatory, and the phrase 'significantly negative' should be used with caution.
  2. [4] Comments labeled 'not applicable' or 'other' are forced to inherit the topic of their parent; this rule may introduce systematic topic-assignment bias and should be justified or validated, especially because the parent-child topic relationship is later used in the homophily analysis.
  3. [Figure 8] In panel (D), the label 'h(∆H s)' appears to be a typo; it should be 'h(∆H)'.
  4. [5.3] The notation in Equation (7) is ambiguous: i and j are used both as bin indices and as midpoints; please clarify the indexing explicitly.
  5. [6.4] The sentence about the linear kernel saying 'the observed values being above the 0.95 quantile of the samples generated by the SIEBC model' is ambiguous: the text is in the paragraph about the linear kernel, but it compares observed homophily to SIEBC samples; clarify which model's samples are used.

Circularity Check

1 steps flagged · score 4.0 of 10

SIEBC's 'predicted homophily' is an in-sample fit to the same expressed sentiments used to estimate its parameters; no other circular steps found.

  1. fitted input called prediction [Section 6.4 and Section 7.3; Equations (7), (8), (11)]
    "Subjecting the predicted sentiments to the analysis of Section 5.3, reveals homophily in Figure 8D, as calculated by Equation (7). ... Even though it is not explicitly fitted on it, the SIEBC model manages to predict the observed homophily between comments well and thus demonstrates its value as a tool for the study of Reddit interactions."

    The SIEBC posterior is sampled 'given the likelihood based on the observed expressed sentiments' (Section 5.4, Equation (8)). The homophily statistic h (Equation (7)) and the Wasserstein metric (Equation (11)) are computed on exactly those same observed expressed sentiment values and their parent-comment pairings. The model-generated sentiments are therefore an in-sample fit to the very data that define the 'predicted' target; the agreement between predicted and observed h is statistically forced by the fitting objective. The phrase 'not explicitly fitted on it' distinguishes only the loss function, not the data source, so the claim that the model 'predicts' the observed homophily is a fitted input presented as an independent prediction.

full rationale

Most of the paper's derivation chain is self-contained. The topic-volume analysis (Section 6.1) compares post counts to external event dates and hospital/dose series, and the 'no initiation contagion' test (Sections 5.2 and 6.2) is a within-user permutation test whose observed rho(i) is data rather than a fitted quantity. The sentiment homophily result (Section 6.3) uses a null model built by permuting observed sentiment pairs, with the statistic h computed from the data; this is also independent of any fitted model. The main circularity is confined to the model-validation language: SIEBC parameters are fitted to the observed expressed sentiment sequences, and the same expressed sentiments define the homophily h and the Wasserstein comparison, so calling the resulting agreement a 'prediction' of homophily is an in-sample fit renamed as prediction. Section 7.6 honestly notes that the latent internal state has no ground-truth comparison, which limits the 'expressed sentiment is a poor proxy' inference but is not itself circular. The broad conclusion 'no social contagion, neither simple, nor complex' overstates what the within-user ordering test can show, because the test never measures exposure to other users' posts; however, this is a construct-validity gap rather than a circular reduction, since the null result is not an input to the test.

Assumptions & free parameters 8 free parameters · 6 assumptions · 1 invented entities

The model and analysis depend on a large number of chosen and fitted quantities: eight explicit free parameters, plus per-user posteriors, plus six implicit modeling assumptions. The internal sentiment is an invented latent entity without external validation. The most consequential free parameters are the negative-day thresholds, which are set post hoc to produce a desired count.

free parameters (8)
  • significantly negative day threshold quantile = 0.275
    Section 5.1: threshold chosen so that one to five negative days can be identified per topic; post-hoc selection.
  • minimum daily comment count = 50
    Section 5.1: chosen to filter days; together with quantile yields desired number of negative days.
  • bounded confidence shape parameter gamma = 50
    Section 5.4: fixed to 50 following ref [67]; controls steepness of smooth kernel.
  • prior means for alpha and sigma = exponential mean 0.5
    Section 5.4: chosen priors for MCMC; affect posterior.
  • prior range for epsilon = Uniform[0,2]
    Section 5.4: chosen so each meaningful epsilon equally likely.
  • per-user model parameters alpha_e, alpha_u, epsilon, sigma_e, sigma_u = posterior samples per user
    Section 5.4: fit to users with at least 40 comments (209, 101, 98 users per topic).
  • minimum comments per user for model fitting = 40
    Section 5.4/Table 3: threshold to select users; changes sample size.
  • homophily bin width = 0.05
    Section 5.3: wH=0.05 used to build histograms and define the homophily measure.
assumptions (6)
  • domain assumption The mbert-ctbt topic model correctly classifies posts into mitigation topics in Dutch, French, English, and German.
    Section 4: topic labels are assigned by a previously trained model; errors propagate to all analyses.
  • domain assumption twitter-roberta-base-sentiment-latest sentiment scores on English posts are valid for Reddit text and English posts represent the community.
    Section 4: only English posts receive sentiment; Dutch and French posts (about 25%) are dropped from sentiment analysis.
  • ad hoc to paper Comments labeled 'not applicable' or 'other' inherit the topic of their parent.
    Section 4: this imputation rule is introduced here; can inflate thread-level topic coherence.
  • domain assumption The null model for initiating contagion (randomizing within-user order while preserving counts) is a valid baseline for social contagion.
    Section 5.2/6.2: the null model only permutes a user's own initiation/participation order; it does not model exposure to other users' posts.
  • ad hoc to paper The smooth bounded confidence kernel B_{alpha,epsilon} with fixed gamma=50 captures sentiment update dynamics.
    Section 5.4: kernel is a modeling choice; linear kernel is formed as a limit.
  • ad hoc to paper The latent internal sentiment u_i is identifiable from expressed sentiment alone under the SIEBC model.
    Section 5.4/7.6: no ground truth for internal state; identifiability is assumed.
invented entities (1)
  • Internal sentiment state u_i[t]
    purpose: Latent variable representing a user's true sentiment, separate from expressed comment sentiment; used to reconstruct internal opinion trajectories.
    Section 5.4: u_i is not directly measurable; the paper acknowledges no ground truth (Section 7.6). It is identified only through the model's assumptions.

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Cite this review

Pith. "Pith review of Social Contagion in COVID-19 Discussions within the Belgian Reddit Community: A Statistical and Modeling Study." pith.science (2026). https://pith.science/paper/6FPUQLOP

@misc{pith2026250520185,
  author       = {Pith},
  title        = {Pith review of: Social Contagion in COVID-19 Discussions within the Belgian Reddit Community: A Statistical and Modeling Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6FPUQLOP}},
  note         = {Machine review of arXiv:2505.20185}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2505.20185 by the authors.

Figure 1
Figure 1. Structure of a Reddit thread. The parent and ancestors of the focal comment are shown. The initiating posts for the [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. The distribution of posts made per user on a double logarithmic scale for the topic ( [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. (A) Schematic representation of the Smooth Internal Expressed Bounded Confidence (SIEBC, Equation (8)). (B) The change in sentiment after applying Bα,ϵ (Equation (9)) as a function of the sentiment difference s2 − s1. mass. A higher value of h thus implies a higher level of homophily. A negative value indicates the opposite, heterophily, where interactions between opposing sentiments are more common than expected. W… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The number of posts per day on (A) lockdowns, (C) masks, (D) vaccination, (B) the number of hospitalisations, and (E) the number vaccination doses in Belgium. All quantities are shown as their two-week rolling mean. Piecewise linear trends are given by dashed lines. Re…
Figure 5
Figure 5. Figure 5: Proportion of users ρ(i) that has at least one initiated post in the first i discussions for the three topics, (A) lockdowns, (B) masks, and (C) vaccination. The full line represents the observed proportion, the dashed line the expected proportion under the null model.…
Figure 6
Figure 6. Figure 6: Normalised histograms of (A,B,C) observed sentiment interactions, and (D,E,F) the difference with the null model for the topics (A,D) lockdowns, (B,E) masks, and (C,F) vaccination. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Measure of homophily h between the sentiment of a comment and its size n ancestral (black) or user (gray) context for the topics (A) lockdowns, (B) masks, and (C) vaccination. findings in Section 6.3. Compared to αe, the values of the internal update strength αu are mo…
Figure 8
Figure 8. Figure 8: Outcomes of the fitted posterior of the SIEBC model. Normalised distributions [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Median internal sentiment ˜u of users over time (full black curve) for the topics (A) lockdowns, (B) masks, and (C) vaccination. The area between the first and third quartile is shaded. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.