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

2026-08-17

AI Summary

This study addresses the overestimation of real-time efficacy in static social media moderation assessments by constructing an empirically calibrated agent-based simulation framework. Utilizing CMA-ES evolutionary strategies to optimize parameters and replicate authentic statistical characteristics, this work quantitatively reveals for the first time how compensatory user reposting significantly undermines governance effectiveness in dynamic moderation environments. The findings confirm that actual moderation performance falls below static estimates due to these adaptive user behaviors. Consequently, this research proposes a high-fidelity simulation methodology that overcomes traditional evaluation biases, providing a reliable dynamic benchmark and theoretical foundation for optimizing content moderation strategies.

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.

Problem

• Content Moderation
• Social Media Simulation
• Real-time Evaluation
• Misinformation

Innovation

• Agent-based Simulation
• Content Moderation
• CMA-ES Calibration
• Dynamic Evaluation
• Misinformation Detection