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About this paper
Enrico Verdolotti, Gianluca Nogara, Luca Luceri, Silvia Giordano
Verdolotti et al. developed and validated an empirically-grounded agent-based social media simulator to evaluate content moderation strategies. Their work demonstrates that static, retroactive evaluations of user bans can significantly overestimate real-time effectiveness for powerful strategies due to compensatory resharing, with overestimations reaching 21% for the TASH-Index and 12% for Random Forest.
Key insights
- Rigorous calibration enables social media simulators to accurately reproduce key statistical signatures of real platform dynamics, including temporal activity, heavy-tailed distributions, and bimodal content quality.
- A validated simulation environment can faithfully replicate the structural and temporal consistency that determines influential users in low-quality content networks, serving as a reliable testbed for moderation strategies.
- Real-time application of user bans triggers compensatory resharing by remaining users, which mitigates the expected reduction in low-quality content, an adaptive feedback loop missed by static evaluations.
Summary by alphaXiv.
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