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"If It Looks Like a User": Measuring Real-Time Moderation Effects via Social Media Simulation

Enrico Verdolotti, Gianluca Nogara, Luca Luceri, Silvia Giordano

Year: 2026Venue: arXiv preprintArea: cs.SIType: PreprintEmbeddings: 45

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%

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Summary

This paper presents a calibrated extension of the SimSoM agent-based social media simulator, grounded in empirical data from the VaccinItaly dataset (COVID-19 vaccine discourse). The authors replace stylized parameters with empirically fitted distributions (GB2 for activity, Beta mixture for quality) optimized via CMA-ES. They validate the simulator against real-world statistical signatures and demonstrate that it reliably proxies for moderation evaluation. Crucially, they compare static (retroactive) versus dynamic (real-time) moderation, finding that static evaluations significantly overestimate the effectiveness of user bans due to compensatory resharing by remaining users.

Entities (8)

SimSoM · softwaremodel · 98%VaccinItaly · dataset · 97%CMA-ES · algorithm · 95%Dynamic Moderation · concept · 93%Static Moderation · concept · 93%TASH-Index · algorithm · 92%GB2 Distribution · statistical-model · 90%NewsGuard · servicetool · 88%

Relation Signals (5)

SimSoM → calibratedusing → VaccinItaly

confidence 95% · We develop a calibrated extension of SimSoM... grounded in a real-world dataset of online vaccine discourse... VaccinItaly

VaccinItaly → providesdatafor → SimSoM

confidence 95% · We ground the simulator in a publicly available longitudinal dataset... VaccinItaly

Static Moderation → overestimates → Dynamic Moderation

confidence 94% · static evaluation significantly overestimates the effectiveness of user bans... static estimates should be read as an upper bound

SimSoM → optimizedby → CMA-ES

confidence 92% · Our approach replaces ad-hoc parametrisations with empirically fitted distributions, optimised via CMA-ES

TASH-Index → usedfor → Misinformation Detection

confidence 90% · apply established misinformation-spreader detection and prevention methods... TASH-Index

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Abstract

Abstract:Agent-based social media simulators offer a controlled environment to study content moderation, yet their value hinges on how faithfully they reproduce real platform dynamics. We develop a calibrated extension of SimSoM, an agent-based model of information diffusion on social networks, grounded in a real-world dataset of online vaccine discourse during the COVID-19 pandemic. Our approach replaces ad-hoc parametrisations with empirically fitted distributions, optimised via CMA-ES (Covariance Matrix Adaptation Evolution Strategy) and validated against real data across temporal, distributional, and structural dimensions. Using this validated simulator, we provide three key contributions. First, we show that the calibrated model reproduces key statistical signatures of the empirical data, including activity distributions, post/reshare ratios, and temporal patterns. Second, we apply established misinformation-spreader detection and prevention methods to both empirical and simulated data, progressively removing top-ranked users and showing that the resulting decline in low-quality content is consistent across the two. Third, comparing static (retroactive) and dynamic (in-simulation) moderation across 30 network realisations, we show that static evaluation significantly overestimates the effectiveness of user bans for the most effective detectors: when moderation is applied in real time, compensatory resharing by the remaining users dampens the expected reduction in low-quality content, so static estimates should be read as an upper bound. These findings highlight the necessity of simulation-based evaluation for content moderation policies and contribute a reusable, empirically grounded simulation framework.

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"If It Looks Like a User": Measuring Real-Time Moderation Effects via Social Media Simulation Enrico Verdolotti 1,2[0009−0009−0330−3537] , Gianluca Nogara 2[0000−0002−4412−131X] , Luca Luceri 3[0000−0001−5267−7484] , and Silvia Giordano 2[0000−0003−2603−9029] 1 Università della Svizzera italiana (USI), Lugano, Switzerland enrico.verdolotti@usi.ch 2 SUPSI, DTI-ISIN, Viganello, Switzerland enrico.verdolotti, gianluca.nogara, silvia.giordano@supsi.ch 3 USC Information Sciences Institute, Marina Del Rey, CA, USA lluceri@isi.edu Abstract. Agent-based social media simulators offer a controlled en- vironment to study content moderation, yet their value hinges on how faithfully they reproduce real platform dynamics. We develop a cali- brated extension of SimSoM, an agent-based model of information diffu- sion on social networks, grounded in a real-world dataset of online vac- cine discourse during the COVID-19 pandemic. Our approach replaces ad-hoc parametrisations with empirically fitted distributions, optimised via CMA-ES (Covariance Matrix Adaptation Evolution Strategy) and validated against real data across temporal, distributional, and struc- tural dimensions. Using this validated simulator, we provide three key contributions. First, we show that the calibrated model reproduces key statistical signatures of the empirical data, including activity distribu- tions, post/reshare ratios, and temporal patterns. Second, we apply es- tablished misinformation-spreader detection and prevention methods to both empirical and simulated data, progressively removing top-ranked users and showing that the resulting decline in low-quality content is con- sistent across the two. Third, comparing static (retroactive) and dynamic (in-simulation) moderation across 30 network realisations, we show that static evaluation significantly overestimates the effectiveness of user bans for the most effective detectors: when moderation is applied in real time, compensatory resharing by the remaining users dampens the expected reduction in low-quality content, so static estimates should be read as an upper bound. These findings highlight the necessity of simulation-based evaluation for content moderation policies and contribute a reusable, empirically grounded simulation framework. Keywords: Social media simulation· Content moderation· Agent-based model· Misinformation· Moderation evaluation· SimSoM 1 Introduction Online misinformation poses a persistent threat to public discourse, particularly during crises such as the COVID-19 pandemic [17,32], yet evaluating the effec- arXiv:2608.16601v1 [cs.SI] 17 Aug 2026 2E. Verdolotti et al. tiveness of interventions remains fundamentally unresolved [29,4]. Social media platforms have responded with a range of moderation interventions, from content removal and demotion to account suspension and deplatforming [16,28,3]. Most empirical assessments of moderation rely on static, retroactive analysis: given a historical dataset, one identifies the most harmful accounts, removes their ac- tivity, and measures the resulting improvement in information quality [8,31,23]. While informative, this approach ignores the feedback loops that arise when moderation occurs in real time: the remaining network adapts, compensatory behaviours emerge, and the information ecosystem reshapes itself around the intervention [14,4,5]. In contrast, agent-based simulation can capture adaptive responses to mod- eration: by embedding policies within a running simulation, one observes emer- gent reactions that static analysis, by construction, cannot. A simulator is only as credible as its calibration, however; as Box noted, “all models are wrong, but some are useful” [2], and a social media simulator’s usefulness depends on how well its dynamics mirror empirical reality. In this paper we present a calibrated, empirically validated extension of Sim- SoM [30], an agent-based model of information diffusion and manipulation on social networks. We ground the simulator in a publicly available longitudinal dataset of Italian-language COVID-19 vaccine discourse [25,23], and replace the original model’s stylised parametrisation with distributions fitted to observed data. In particular, user activity levels are drawn from a Generalized Beta of the Second Kind (GB2) distribution [21] whose parameters are optimised via CMA-ES [13], and content quality follows a bimodal Beta mixture calibrated by grid search on the empirical credibility distribution. We address three research questions. – RQ1: Can the calibrated SimSoM reproduce the statistical signatures of real platform dynamics? – RQ2: Does the evaluation of moderation strategies on simulated data yield effects consistent with those measured on empirical data? – RQ3: How does the effect of a moderation intervention applied dynami- cally (in-simulation, allowing the network to react) compare with its static (retroactive) evaluation? The intervention itself is a single, one-shot user ban (Section 6); what is dy- namic in RQ3 is not the policy but the agents’ adaptive response to it, which static removal cannot capture. We focus on user-centred moderation (account bans), rather than content-centred actions (labelling, demotion): account-level interventions such as suspension and deplatforming are among the most con- sequential, widely deployed levers, and are precisely where the compensatory network responses we study arise. Our contributions are threefold. First, we propose an empirically grounded calibration and validation methodology for social media simulators: user activ- ity is fitted to a GB2 distribution via CMA-ES, content quality follows a bi- modal Beta mixture calibrated by grid search, and validation spans temporal, distributional, and structural dimensions. We show that the calibrated model Real-Time Moderation Effects via Social Media Simulation3 closely reproduces activity distributions, post/reshare ratios, and quality pro- files of the empirical data (RQ1). Second, we provide evidence that simulated data produced by the calibrated model can reliably serve as a proxy for evaluat- ing moderation strategies: four independent detection methods [31] yield com- parable user-removal effectiveness curves on empirical and simulated data, with confidence intervals consistently overlapping (RQ2). Third, using a paired com- parison over 30 network realisations, we show that static, retroactive evaluation significantly overestimates the impact of user bans for the effective detectors (and not for the ineffective ones): when moderation is applied dynamically, com- pensatory resharing by the remaining users dampens the expected reduction in low-quality content, so static evaluation acts as an upper bound (RQ3). Relation to prior work. From our recent work on misinformation-spreader rank- ing [31] we inherit only the inputs to RQ2: the four spreader-ranking methods and the VaccinItaly credibility-annotation pipeline, reused unchanged as off-the- shelf detectors. Everything else is novel: the calibrated SimSoM extension (GB2 activity via CMA-ES, the bimodal-Beta quality mixture, the correlation/appeal structure), the validation protocol of Section 4, and, most importantly, the static- vs-dynamic comparison of Section 6. In short, [31] provides whom to remove; this paper measures what happens when they are removed in real time. 2 Related Work Social media simulation. Agent-based models have a long tradition in compu- tational social science [11,10]. SimSoM [30] introduced a minimalistic model of information diffusion on directed networks, demonstrating how adversarial tac- tics (infiltration, flooding, deception, and coordinated inauthentic behaviour) degrade information quality. More recent work has extended agent-based mod- elling to cross-platform diffusion [22], language-sensitive cloning of online com- munities [26], and LLM-powered content generation [18]. However, existing sim- ulators typically rely on stylised parameter choices, are rarely validated against real-world intervention outcomes, and almost exclusively evaluate moderation in static, retrospective settings. In contrast, we develop a simulator that is fully cal- ibrated on empirical data, validate it against empirical targeted-removal exper- iments, and use it to directly compare static and dynamic moderation regimes. Misinformation spreading and detection. The empirical study of misinformation has established that false content spreads faster and more broadly than truth- ful information [32], and that echo chambers amplify polarisation [5]. Identify- ing the most influential misinformation spreaders is a prerequisite for effective moderation. Verdolotti et al. [31] proposed a behavioural-archetype framework distinguishing super-spreaders, amplifiers, and coordinated accounts, introduc- ing time-aware ranking methods such as the TASH-Index that outperform static centrality measures. Coordinated inauthentic behaviour detection [24,7,20,19] complements individual-level ranking by identifying organised campaigns and automated accounts. 4E. Verdolotti et al. Content moderation. Schneider and Rizoiu [28] provided empirical evidence that moderation is effective at reducing harmful content exposure, but noted sub- stantial variation across platforms and policy designs. Jahn et al. [16] studied friction-based interventions within a simulation framework, showing that small design nudges can curb misinformation spread. Truong et al. [29] audited take- down delays across major platforms and demonstrated that even moderate de- lays drastically reduce moderation effectiveness. Network dismantling [27], i.e. the progressive removal of nodes to maximally disrupt a network, provides the mathematical backbone for evaluating targeted user removal strategies. Simulation validation. Validating agent-based models against empirical data is a well-recognised methodological challenge [34,6]. Common approaches include distributional comparison, temporal pattern matching, and sensitivity analysis. Our work extends this tradition by proposing a multi-dimensional validation protocol that jointly assesses activity distributions, temporal dynamics, content composition, and moderation outcomes. 3 Data and Simulation Framework 3.1 Empirical Data: VaccinItaly We ground our simulator in VaccinItaly [25,23], a longitudinal dataset of Italian- language COVID-19 vaccine discourse collected from December 2020 to Novem- ber 2021 (≈ 306 days). VaccinItaly tracks public conversations about vaccines on Twitter, gathered through the streaming API with a curated, routinely updated list of Italian vaccine-related keywords and hashtags (e.g. vaccino, AstraZeneca, Pfizer, greenpass) so as to track trending terminology over the campaign; we use the Twitter posts and retweets, which map naturally onto the simulator’s post and reshare actions. The resulting subset comprises 819,952 actions (posts and reshares) by 74,234 unique users, annotated with per-item credibility scores derived from source reliability ratings. Content credibility is determined by ex- tracting URLs from each post and querying the NewsGuard reliability index of the associated domains, following the methodology of DeVerna et al. [8] and Verdolotti et al. [31]. We adopt a normalised credibility threshold of 0.39 to distinguish low-quality (LQ) from high-quality content. 4 3.2 SimSoM and Our Extensions SimSoM [30] is an agent-based model in which users interact on a directed fol- lower network. At each time step, active users either create original posts or reshare content from their feed. A recommender system populates each user’s feed with a mixture of in-network (friends’ posts) and out-of-network (trending) content, ranked by appeal and recency. The original model uses a power-law activity distribution and uniform quality assignment. 4 Throughout this paper, we use low-quality as a generalisation of misinformation: while prior work on VaccinItaly focused specifically on vaccine misinformation [31,8], our simulator and evaluation framework apply to any content quality dimension. Real-Time Moderation Effects via Social Media Simulation5 We extend SimSoM along four axes, replacing stylised assumptions with em- pirically calibrated mechanisms: (i) Activity generation via GB2. We model per-user mean activity with a Gen- eralized Beta of the Second Kind (GB2) distribution [21], constructed as X = b· (U/(1− U )) 1/a where U ∼ Beta(p,q). The four parameters (a,b,p,q) control tail heaviness, scale, mass near zero (inactive users), and tail decay, respectively. We optimise these jointly with four temporal parameters (user inertia, user vari- ability, global inertia, global variability) using CMA-ES [13] via Optuna [1], min- imising a composite fitness function over percentile errors, autocorrelation, and daily totals on VaccinItaly. CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a derivative-free, population-based optimiser that samples candi- date parameter vectors from a multivariate Gaussian and iteratively adapts its mean and covariance towards the better candidates, making it well suited to our non-convex, noisy, gradient-free fitness landscape. The fitted values are a=0.472, b=0.008, p=2.439, q=3.106. Daily activity counts are then sampled as n(t) ∼ Poisson( ̄ λ· s u (t)· s g (t)), where ̄ λ is the GB2-drawn mean activity rate, and s u (t), s g (t) are user-level and global temporal state multipliers evolving as random walks with calibrated inertia. (i) Quality distribution. Content quality is drawn from a bimodal Beta mixture: 18% of users sample from a low-quality component (μ=0.14, spread 12) and 82% from a high-quality component (μ=0.84, spread 8). Parameters were selected by grid search over 5,040 combinations, minimising weighted error on per-action quality fractions from VaccinItaly. (i) Correlation structure. We induce empirically motivated correlations via the Iman-Conover method [15]: activity↔in-degree (ρ=0.6), activity↔quality (ρ=−0.14), and post-propensity↔activity (ρ=0.13). Action type probabilities (post vs. reshare) follow a Dirichlet distribution with α=(0.075, 0.095), yielding the bimodal specialisation pattern observed in real data. (iv) Content appeal and critical filtering. User-level content appeal is rank- correlated with in-degree via a right-skewed transform. Low-quality content re- ceives a sensationalism variance boost (factor 2.5 for q < 0.35), in line with the empirical observation that a small fraction of misinformation achieves dispropor- tionate virality. A critical filter governs reshare selectivity: extreme users (very low or very high quality) preferentially reshare quality-similar content, while moderate users are less selective (selectivity=0.97, extremism_weight=0.25). Network and experimental configurations. All simulations run on directed net- works with average out-degree 3, loaded from pre-generated graphs consistent with empirical degree distributions, and span 306 days to match the VaccinItaly observation window. We employ two network configurations for different exper- imental purposes. For statistical validation and dismantling comparison (RQ1– RQ2), we use 90,000-node networks. The size is dictated by the GB2 mass near zero: a non-negligible fraction of agents draws an activity rate so low that they perform no action during the 306-day window (inactive lurkers). Since VaccinI- 6E. Verdolotti et al. taly by construction only contains users who posted at least once, calibrating on a 74k-node graph would yield a systematically smaller active population. We therefore pick the smallest network size whose expected active population matches the empirical 74k under the calibrated GB2: 90k nodes (cf. Table 1, “To- tal users”: 72.9k± 1.6k). Lurkers do not affect dismantling: by construction, they cannot appear in any LQ reshare network. Each of the 50 runs generates a fresh network, so results are robust to network realisation. For the static-vs-dynamic moderation comparison (RQ3), we use 30 independently generated 50,000-node networks with the same calibrated parameters, as a controlled substrate whose fidelity is already established at 90k (Section 4). Each network is run through every condition (no moderation and each ranking method), so the static and dynamic regimes of a given method are evaluated on the same topology and compared pairwise, with the network realisation as the matching unit. This de- sign removes the (large) between-network variance while still probing robustness across 30 realisations. 4 Simulating Real Data (RQ1) We validate the calibrated simulator by comparing 50 independent simulation runs, each on a freshly generated 90k-node network, against VaccinItaly across multiple statistical dimensions, to assess whether it reproduces the empirical signatures relevant to information diffusion and moderation. Table 1 summarises the key metrics. Temporal dynamics. Figure 1 shows the 7-day rolling average of actions per user for VaccinItaly and the simulated runs (per-user normalisation makes the 90k- node simulation and the 74k-user dataset directly comparable). To convey both typical and best-case behaviour, the figure overlays three curves: the real series; the median across the 50 runs together with its 5th–95th percentile envelope (a robust, outlier-resistant summary of the run-to-run spread, not a confidence interval on the mean, and hence wide by design); and the single simulation run closest to the real series, selected as the one with smallest mean absolute er- ror (MAE). The aim is qualitative: rather than matching the exact trajectory, we ask whether the simulator endogenously produces plausible dynamics. Two facts support this: the empirical series stays within the run-to-run envelope for essentially the whole window (so it is a plausible draw, not an outlier), and the closest run tracks it over long stretches, reproducing sustained bursts of com- parable magnitude (e.g. days 150–185) without being a lucky aberration, as it lies inside the band. The simulated temporal autocorrelation matches the real data within 2% at lags 1–5. The largest residual departures, most visibly around days 80–130, correspond to exogenous, event-driven surges in the real discourse, e.g. the temporary suspension of the AstraZeneca vaccine in March 2021, the spring vaccination-campaign ramp-up, and the introduction of the “Green Pass” in summer 2021. These spikes are driven by external news shocks that lie out- side the model’s generative mechanism (the simulator has no exogenous-event channel), so neither the median nor any single run is expected to time-align with them; the calibrated dynamics track the baseline trend and its dispersion but, Real-Time Moderation Effects via Social Media Simulation7 Table 1. Summary statistics for real and simulated datasets. Dataset statistics: total actions (posts and reshares), unique users, observation window. Content composition: orphan rate = fraction of reshares whose parent post is absent from the collected data; post/reshare fraction = share of actions that are posts vs. reshares; unique tar- gets ratio = fraction of distinct content items ever reshared. User activity: per-user action counts (median, p90), activity inequality (Gini), burstiness [12] of inter-event times, and median response time = delay between a post and its first reshare (see the sequential-scheduling discussion in the text). Simulation values are mean± std across 50 independent 90k-node runs. MetricRealSimulated Dataset statistics Total actions819,952973,539 ± 73,558 Total users74,23472,915 ± 1,605 Duration (days)306.5306.0 ± 0.0 Content composition Orphan rate0.1140.000 ± 0.000 Post fraction0.5470.577 ± 0.011 Reshare fraction0.4530.423 ± 0.011 Unique targets ratio0.1220.113 ± 0.005 User activity Actions/user (median)1.0002.980 ± 0.141 Actions/user (p90)14.00021.480 ± 1.199 Gini coefficient0.8340.788 ± 0.006 Burstiness0.5210.419 ± 0.009 Response time, h (median)0.1064.872 ± 0.177 by construction, do not reproduce individual real-world events. This gap con- cerns the timing of bursts, not the daily-aggregated quantities (activity level, post/reshare composition, LQ fraction) that drive the moderation analysis in Section 6. Activity distributions. The simulated per-user activity distribution preserves the heavy-tailed character of the empirical data (Gini 0.788 vs. 0.834). The discrep- ancy at the median (2.98 vs. 1.0 actions/user) reflects the simulator’s fully- connected observation window: all generated actions are recorded, whereas the real dataset only captures actions matching the collection query. Post and reshare fractions are closely matched (simulated: 57.7%/42.3% vs. real: 54.7%/45.3%). The one notable gap is the median response time (4.9 h simulated vs. 0.1 h real, i.e. the delay between a post and its first reshare): the simulator’s sequential scheduling prevents instantaneous within-tick reactions, inflating short intervals. Since moderation is evaluated through daily LQ-fraction changes over a 120-day window, this affects sub-daily timing, not the aggregate quantities used in the moderation experiments of Section 6. Quality profile. The per-action credibility distribution is bimodal in both datasets, with aligned low- and high-quality peaks and matching mass (mean and std differ by <1 pt on the [0–100] scale): real and simulated data agree on the quantities that drive moderation analysis (bimodal shape, LQ/HQ mass ratio, and the 8E. Verdolotti et al. 050100150200250300 Elapsed days 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Actions per user (7-day rolling avg) Real dataMedian (50 runs)Most similar run (by MAE) Fig. 1. Temporal comparison: 7-day rolling average of actions per user. Real series (dark), median across the 50 runs (dashed) with its 5th–95th percentile envelope (shaded band), and the single run closest to the real series by mean absolute error (solid). LQ-fraction time series of Section 6). The real distribution is discrete (News- Guard assigns one credibility score per domain), whereas the simulator samples a continuous Beta mixture. Content composition. The unique targets ratio is closely matched (0.113 vs. 0.122); the simulated orphan rate is zero by construction (the simulator tracks all content internally), while VaccinItaly shows 11.4% orphan reshares due to incomplete data collection. Overall, the calibrated simulator reproduces the es- sential statistical properties that directly govern information diffusion and mod- eration outcomes of VaccinItaly, supporting its use as a testbed for moderation experiments. 5 Moderation Strategies: Real vs. Simulated Data (RQ2) Having established statistical fidelity, we ask whether moderation strategies eval- uated on simulated data yield effects consistent with empirical data. We apply four misinformation-spreader ranking methods (TASH-Index, Random Forest, Coordination Centrality, Repost Count [31]) to both VaccinItaly and the 50 simulated runs, then perform targeted user removal (network dismantling) [8], testing whether the simulator concentrates influence on the same users as real data. Ranking methods. TASH-Index [31] is a time-aware social h-index weighting re- cent activity via exponential moving averages; Random Forest combines archetype features [31] in a supervised model; Coordination Centrality uses eigenvector cen- trality on a TF-IDF resharing-similarity graph [24]; Repost Count is a frequency baseline on low-credibility reshares. Real-Time Moderation Effects via Social Media Simulation9 Dismantling protocol. Following [8,31], we build the LQ reshare network (an edge u→v of weight w means v reshared low-quality content authored by u w times), remove users in ranked order, and track the remaining LQ reshare weight [27]; simulated curves are median ± 5th/95th percentile across the 50 runs. Removal curves. Figure 2 shows the dismantling of the LQ reshare network. Across all four methods, the shape and rate of decline match closely between VaccinItaly and simulation, with simulated CIs overlapping the real-data curve, consistent with the simulator reproducing the structural concentration of misin- formation spreading and thus the relative effectiveness of targeted user removal. 0 10 0 10 1 10 2 10 3 10 4 Nodes removed (log scale) 0.0 0.2 0.4 0.6 0.8 1.0 Remaining LQ fraction Real Data 0 10 0 10 1 10 2 10 3 10 4 Nodes removed (log scale) 0.0 0.2 0.4 0.6 0.8 1.0 90K Agents Sim (n=50 runs) Ground TruthTASH-indexRandom ForestRepost CountCoordination Centrality Fig. 2. Targeted user removal: remaining fraction of LQ reshare weight as users are removed one by one in ranked order, for real (solid) and simulated (shaded CI) data. Temporal effectiveness. A complementary question is how the top-k users histori- cally contributed to LQ reshares at different time points in the dataset. Figure 3 fixes the banned set (top 5 by each ranking) and measures the percentage of LQ reshares attributable to them at 1, 7, 30, 60, and 120 days after a reference moderation day (day 186, the same split used in RQ3). On real data, the ground- truth drops from ∼65% at day 1 to ∼40% at day 120, as other users gradually compensate. TASH-Index and Random Forest track this decay closely; Repost Count and Coordination Centrality remain near zero, confirming their inability to capture the dominant spreading structure. The simulated data reproduces this temporal decay, with confidence intervals overlapping the real-data curves at every horizon. 173060120 Days after moderation 0 20 40 60 80 100 LQ reshares removed (%) Real Data 173060120 Days after moderation 0 20 40 60 80 100 90K Agents Sim (n=50 runs) Ground TruthTASH-indexRandom ForestRepost CountCoordination Centrality Fig. 3. Temporal effectiveness of banning the top-5 ranked users: percentage of LQ reshares attributable to banned users at increasing days after moderation, for real (left) and simulated (right, with CI) data. 10E. Verdolotti et al. These results validate the simulated data as a reliable proxy for evaluating moderation strategies: the simulator preserves not only aggregate statistics but also the structural and temporal consistency that determine which users are most influential in LQ content networks. 6 Static vs. Dynamic Moderation (RQ3) The central question is whether static moderation evaluation (removing users retroactively from a completed dataset) faithfully predicts the effect of dynamic moderation, where the remaining users adapt their resharing in response to the intervention, which static post hoc removal cannot capture. We compare the two regimes across 30 independent 50k-node networks (Section 3.2). Because both regimes of a method run on the same network, we compare them pairwise (net- work as the matching unit), which cancels the large between-network variance and isolates the moderation effect. Experimental setup. We run each simulation for 306 days with dynamic mod- eration activated at day 186 (approximately the 60/40 pre/post split in Vac- cinItaly). At the moderation point, the top-5 users identified by each ranking method are permanently banned: their activity rate is set to zero, and their con- tent is excluded from all feeds. For the static condition, we take each network’s unmoderated run and retroactively remove the top-5 users’ post-moderation ac- tions. For each method, we report the change in mean daily LQ fraction (∆) under each regime and the within-network gap (static−dynamic) with a 95% bootstrap confidence interval [9] and a Wilcoxon signed-rank test [33] over the 30 networks. Content-level comparison. Table 2 reports the paired static−dynamic gap in mean daily LQ fraction across the 30 networks. Table 2. Static (S) vs. dynamic (D) moderation across 30 independent 50k-node net- works (moderation at day 186, top-5 banned). ∆: change in mean daily LQ fraction (↓ = less misinformation). Gap = S−D with a 95% bootstrap CI over networks; a negative gap means static overestimates real-time effectiveness (an upper bound). p: Wilcoxon signed-rank test (H 0 : median gap = 0). Bold: significant at the 5% level. ∆ and the gap are rounded independently; the gap, CI and p are computed on unrounded values, so column differences may differ by up to 0.001. Method∆ S ∆ D Gap [95% CI]p TASH-Index↓0.075 ↓0.059 -0.016 [-0.024, -0.009] <0.001 Random Forest↓0.074 ↓0.066 -0.009 [-0.016, -0.002] 0.050 Repost Count↓0.006 ↓0.005-0.000 [-0.009, +0.008]0.715 Coordination Centrality ↓0.003 ↓0.004 +0.002 [-0.011, +0.012]0.119 Two findings stand out. First, for every method, static evaluation reports a reduction at least as large as the dynamic one (gap ≤ 0 or null): it never significantly under-states effectiveness, so it acts as an upper bound. Second, and crucially, the overestimation is statistically significant precisely for the two effective rankers, TASH-Index (gap−0.016, p < 0.001) and Random Forest (gap Real-Time Moderation Effects via Social Media Simulation11 0.02 0.00 0.02 0.04 TASH-IndexRandom Forest 050100150200250300 Elapsed days 0.02 0.00 0.02 0.04 Repost Count 050100150200250300 Elapsed days Coordination Centrality Running LQ gap: static dynamic Mean running gap (95% bootstrap CI) t mod = 186 Fig. 4. Running within-network static− dynamic gap in daily LQ fraction per ranking method (mean over the 30 networks, 95% bootstrap CI; dotted line: t mod = 186). Ref- erenced to each network’s pre-moderation baseline, the gap is zero before moderation and afterwards converges to the paired gap of Table 2. Negative values mean static evaluation overestimates real-time effectiveness. Only TASH-Index and Random For- est separate from zero; the other two rankers stay within the 95% CI of zero. −0.009, p = 0.05), and indistinguishable from zero for the two ineffective ones (Repost Count, Coordination Centrality; p > 0.1). In relative terms, real-time adaptation erases about 21% of TASH-Index’s and 12% of Random Forest’s predicted reduction. Before the moderation day, the paired gap is statistically zero for all methods (a balance check); it departs from zero only afterwards. Interpretation over time. Figure 4 plots the running static−dynamic gap over time. Before the moderation day, it hovers at zero for every method, a balance check confirming the two conditions are equivalent on the same network; after the ban, it separates from zero only for the effective rankers (TASH-Index, Random Forest), settling at a negative value, while staying at zero for the ineffective ones. A negative gap means static evaluation reports a larger LQ reduction than the live simulation delivers: once top spreaders are removed, the remaining users partially compensate by resharing other LQ sources, a feedback invisible to static retrospective removal. Because the difference is taken within each network, the between-network variance cancels, and the band is tight. Network-level comparison. The same pattern holds, more sharply, at the network level: for the effective rankers static evaluation overestimates the reduction in LQ-reshare edges and total weight by 18–25 p (e.g. TASH-Index edges: ↓36% static vs.↓11% dynamic), whereas for the ineffective rankers the gaps stay within a few percentage points with no qualitative reversal, and the largest weakly connected component barely moves under any regime. 12E. Verdolotti et al. 7 Discussion Validity and non-circularity of the calibrated SimSoM. Principled parameter fit- ting (GB2, CMA-ES, grid search) yields a simulator that closely reproduces real dynamics. A natural concern is circularity: do the results merely reflect baked-in assumptions? We address this in three ways. First, the quality distribution is fitted to the empirical credibility distribution, reproducing observed proportions rather than arbitrary choices. Second, the identity of top spreaders is not prede- termined but emerges from activity, network position, appeal, and feed dynam- ics. Third, the targeted-removal validation (RQ2) shows that the same ranking methods identify comparably influential users in real and simulated data, so the emergent spreading structure matches reality. Static evaluation as an upper bound. The overestimation by static analysis (RQ3) has direct policy implications: decision-makers relying on retroactive evaluation may over-credit planned interventions. The gap is significant exactly for the rankers a practitioner would deploy and null for the ineffective ones, which pre- empts the concern that it might matter only for poor detectors. The results suggest a compensatory resharing mechanism: the remaining users fill the void left by banned spreaders, an adaptation invisible to static removal that comple- ments prior work on moderation effectiveness [28] and friction interventions [16]. Static evaluation should therefore be read as an upper bound on real-time effec- tiveness, loosest for the strong detectors that matter most in practice. Limitations and future work. The simulator does not model exogenous events, platform algorithm changes, or user creation/deletion, and its networks (50k– 90k nodes) are smaller than major platforms. The grounding is specific to one platform (Twitter), language (Italian), topic (COVID-19 vaccines), and window (2020–2021), so the calibrated parameters and absolute effect sizes should trans- fer with caution; the methodology and the qualitative compensatory mechanism, however, rest on generic properties of feed-based networks (heavy-tailed activity, redundant resharing paths) and should recur elsewhere, with magnitude varying by network structure, moderation latency, and cultural context. Future work should test other interventions (labelling, friction, downranking) and study how compensatory dynamics scale across networks and platforms. 8 Conclusion We presented a calibrated, validated extension of SimSoM and used it as a mod- eration testbed on VaccinItaly. Beyond reproducing key statistical signatures of real dynamics (RQ1), the simulator reproduces the observed effectiveness of four independent moderation strategies (RQ2), showing that it captures the influence structure behind intervention outcomes rather than merely matching aggregate distributions. This intervention-level validity is what licenses the central finding: when moderation is applied dynamically, static evaluation significantly overes- timates the effectiveness of user bans (RQ3). More broadly, once a simulator reproduces observed moderation outcomes, it can probe adaptive effects and counterfactual policies inaccessible to retrospective analysis. Real-Time Moderation Effects via Social Media Simulation13 Acknowledgments This work was partially supported by the Swiss National Science Foundation (grant number CRSII5_209250). Disclosure of Interests. The authors have no competing interests to declare that are relevant to the content of this article. References 1. 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