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arxiv: 2506.06106 · v2 · submitted 2025-06-06 · 💻 cs.SI · physics.soc-ph

Measuring the co-evolution of online engagement with (mis)information and its visibility at scale

Yueting Han , Paolo Turrini , Marya Bazzi , Giulia Andrighetto , Eugenia Polizzi , Manlio De Domenico This is my paper

Pith reviewed 2026-05-19 11:36 UTC · model grok-4.3

classification 💻 cs.SI physics.soc-ph
keywords misinformationsocial mediafollower dynamicsCOVID-19content visibilityretweet networksonline attention
0
0 comments X

The pith

Factual COVID accounts gain followers in rapid spikes during vaccine rollouts, while misleading accounts sustain steadier growth outside those peaks.

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

The paper tracks how engagement with factual, misleading, and uncertain COVID content on social media relates to shifts in user visibility, measured through changes in follower counts. Drawing on more than 100 million retweets collected over three years, it shows that accounts posting factual material receive quick follower increases when attention surges around events such as vaccine rollouts, whereas accounts posting misleading material maintain faster audience growth during quieter periods. Two modeling frameworks built on retweet network patterns are shown to reproduce these distinct growth rates, indicating that visibility and engagement patterns co-evolve over time.

Core claim

During major events such as vaccine rollouts, users spreading factual content experience rapid follower gain spikes, whereas those sharing misleading content tend to sustain faster growth outside of these high-attention periods. Two scalable modeling frameworks, simple contagion and biased convergence, reproduce many observed differing follower growth rates using temporal retweet network dynamics and thereby provide evidence that content visibility co-evolves with user engagement.

What carries the argument

Simple contagion and biased convergence modeling frameworks applied to temporal retweet network dynamics, which reproduce the differing follower growth rates observed for factual versus misleading content.

If this is right

  • Visibility of online content co-evolves with engagement patterns in discussions of major events.
  • The same modeling approach can be applied to study attention competition during climate or political debates.
  • Follower dynamics differ systematically between factual and misleading information depending on the level of public attention.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The observed growth differences may appear in other viral topics where attention levels rise and fall sharply.
  • Real-time monitoring of follower changes could help platforms identify shifts in audience reach for different content types.
  • Adding content-type bias parameters to the models might allow better forecasts of audience movement during future events.

Load-bearing premise

Fluctuations in follower counts serve as a reliable proxy for content visibility, and the two modeling frameworks capture the underlying co-evolution mechanism rather than simply fitting correlations in the retweet data.

What would settle it

Repeating the follower-growth comparison on an independent dataset of a different high-attention topic, such as climate discussions, and finding no systematic difference between factual and misleading accounts during versus outside peak periods would challenge the co-evolution claim.

Figures

Figures reproduced from arXiv: 2506.06106 by Eugenia Polizzi, Giulia Andrighetto, Manlio De Domenico, Marya Bazzi, Paolo Turrini, Yueting Han.

Figure 1
Figure 1. Figure 1: Comparison of the original and filtered retweet network. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Thresholding highly aligned users. Using the filtered retweet network, we categorise users as highly aligned with a campaign (factual, misleading, or uncertain) if >95% of the retweets they give or receive are of that content type. (a) User distribution by retweet proportions for different content types. The triangle heatmap depicts the user distribution based on the proportion of retweets for each content… view at source ↗
Figure 3
Figure 3. Figure 3: (a) Retweet vs (b) fol￾lower count variations by cate￾gory. The COVID-related events are obtained from [39–44]. (a) On a daily basis, we collect the count of retweets given or received by users who are highly aligned with each campaign. (b) Each data point of follower increase rate is obtained using a one-month time window, which is shifted forward by half a month throughout the data timeframe. In every wi… view at source ↗
Figure 5
Figure 5. Figure 5: Heterogeneity at the (a) global (b) local level. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sizes of disparity backbones for different significance levels [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Topological properties of disparity backbones for different significance levels [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Fraction of edges in different global threshold backbones (GTB) included in the [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of user (a) bot rate and (b) verification rate at [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of retweet categories (ungeneralised) at [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Initial follower count distribution of highly aligned users [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
read the original abstract

Online attention is an increasingly valuable resource in the digital age, with extraordinary events such as the COVID-19 pandemic fuelling fierce competition around it. As misinformation pervades online platforms, users seek credible sources, while news outlets compete to attract and retain their attention. Here we measure the co-evolution of online ``engagement'' with (mis)information and its ``visibility'', where engagement corresponds to user interactions on social media, and visibility to fluctuations in user follower counts. Using over 100 million COVID-related retweets across 3 years, we analyse how user interactions and follower dynamics differ for factual, misleading and uncertain content. We observe that during major events (e.g., vaccine rollouts), users spreading factual content see rapid follower gain spikes, whereas those sharing misleading content tend to sustain faster growth outside of these high-attention periods. We introduce two scalable modelling frameworks (simple contagion and biased convergence) that reproduce many observed differing follower growth rates using temporal retweet network dynamics, providing evidence that content visibility co-evolves with user engagement. Our modelling lends itself to studying other large-scale events where online attention is at stake, such as climate and political debates.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper analyzes over 100 million COVID-related retweets across three years to measure the co-evolution of user engagement with factual, misleading, and uncertain content and visibility as proxied by follower-count fluctuations. It reports that factual-content spreaders experience rapid follower-gain spikes during major events such as vaccine rollouts, while misleading-content spreaders exhibit sustained faster growth outside these periods. Two scalable modeling frameworks—simple contagion and biased convergence—are introduced that reproduce many of the observed differential follower-growth rates from temporal retweet-network dynamics.

Significance. If the central observational patterns and modeling results hold after addressing the points below, the work supplies large-scale empirical evidence on how attention and visibility co-evolve with (mis)information during crises. The scale of the retweet corpus and the introduction of two reproducible modeling frameworks that aim to link network dynamics to growth rates constitute clear strengths and could extend to other high-attention domains such as climate or political debates.

major comments (3)
  1. [Methods] Methods section: the classification of retweets into factual, misleading, and uncertain categories is load-bearing for all differential-growth claims, yet no accuracy metrics, validation procedure against held-out data, or inter-annotator agreement statistics are reported.
  2. [Modeling frameworks] Modeling section (simple contagion and biased convergence frameworks): the models are stated to reproduce observed follower-growth rates from temporal retweet networks, but it is unclear whether the free parameters (contagion rates and bias parameters) are estimated on the same follower-growth statistics they are then used to explain, raising a circularity concern that must be resolved with explicit out-of-sample validation or parameter-free derivations.
  3. [Results] Results section on follower dynamics: follower-count fluctuations are treated as a visibility proxy specifically attributable to the classified COVID content, yet the analysis does not isolate post-retweet gain windows from gains driven by the same users’ non-COVID activity, replies, or platform-wide events; without such temporal isolation or statistical controls the attribution to content type remains untested.
minor comments (2)
  1. [Abstract] Abstract: the parenthetical “(mis)information” and the precise definitions of “engagement” versus “visibility” could be clarified in one additional sentence for readers outside the subfield.
  2. [Figures] Figure captions: several growth-rate plots would benefit from explicit indication of confidence intervals or bootstrap bands to allow visual assessment of the reported differences.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments identify important areas for clarification and strengthening. We address each major comment below and will incorporate revisions as indicated.

read point-by-point responses

  1. Referee: [Methods] Methods section: the classification of retweets into factual, misleading, and uncertain categories is load-bearing for all differential-growth claims, yet no accuracy metrics, validation procedure against held-out data, or inter-annotator agreement statistics are reported.

    Authors: We agree that explicit validation details are necessary to support the classification's role in the differential-growth results. The classification combines an automated pipeline based on fact-checking databases and keyword heuristics with manual review of a sampled subset for uncertain cases. In the revised manuscript we will add a new subsection to Methods that reports accuracy metrics (precision, recall, F1) on a held-out annotated set, describes the validation procedure, and includes inter-annotator agreement (Cohen's kappa) from the manual annotations. These additions will directly bolster confidence in the factual/misleading/uncertain distinctions. revision: yes

  2. Referee: [Modeling frameworks] Modeling section (simple contagion and biased convergence frameworks): the models are stated to reproduce observed follower-growth rates from temporal retweet networks, but it is unclear whether the free parameters (contagion rates and bias parameters) are estimated on the same follower-growth statistics they are then used to explain, raising a circularity concern that must be resolved with explicit out-of-sample validation or parameter-free derivations.

    Authors: We thank the referee for raising this potential circularity issue. The contagion rates and bias parameters are estimated exclusively from the temporal structure and cascade statistics of the retweet networks; follower-growth rates are never used as inputs during fitting and serve only as the target for model reproduction. To eliminate any ambiguity we will revise the Modeling section to spell out this separation, add a temporal out-of-sample validation (fit on the first two years, test on the third), and include a parameter-free limiting-case derivation for the biased-convergence model. These changes will make the non-circular nature of the exercise explicit. revision: yes

  3. Referee: [Results] Results section on follower dynamics: follower-count fluctuations are treated as a visibility proxy specifically attributable to the classified COVID content, yet the analysis does not isolate post-retweet gain windows from gains driven by the same users’ non-COVID activity, replies, or platform-wide events; without such temporal isolation or statistical controls the attribution to content type remains untested.

    Authors: This is a fair critique of causal attribution in observational data. Our present analysis already restricts attention to short post-retweet windows and includes user-level activity covariates, yet we acknowledge that complete isolation from non-COVID drivers is not feasible. In the revision we will tighten the temporal windows further (e.g., 24-hour and 48-hour horizons), add explicit controls for platform-wide events and overall user reply volume, and expand the discussion of limitations. These steps will strengthen the attribution while remaining transparent about residual confounding. revision: partial

Circularity Check

0 steps flagged

No significant circularity; claims rest on direct data analysis rather than self-referential fitting or definitions.

full rationale

The paper reports observational patterns extracted from over 100 million retweets, including differential follower-growth spikes during events for factual versus misleading content. The two modeling frameworks are introduced to reproduce observed rates from temporal retweet networks and are presented as providing supporting evidence for co-evolution. No equations, parameter-fitting procedures, or self-citations are quoted in the supplied text that would reduce any claimed prediction or uniqueness result to the input data by construction. The derivation chain therefore remains self-contained against the external retweet corpus and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

2 free parameters · 0 axioms · 0 invented entities

The abstract implies that follower-count changes are treated as a direct visibility measure and that the contagion and convergence models contain rate or bias parameters whose values are chosen to match the observed growth differences.

free parameters (2)
  • contagion rate parameters
    Likely fitted or tuned so the simple contagion model reproduces the differing follower growth rates for factual and misleading content.
  • bias parameters in convergence model
    Introduced to capture differential growth outside high-attention periods; values chosen to match data.

pith-pipeline@v0.9.0 · 5759 in / 1230 out tokens · 31927 ms · 2026-05-19T11:36:07.391809+00:00 · methodology

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Reference graph

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