Beyond Corrective Content: Social Hypergraphs for Misinformation Intervention

By the time misinformation is detected, users may already believe it, discuss it, or pass it on. Correcting misinformation after exposure therefore requires deciding which users or audiences need attention, what corrective information to provide, and how to adapt as their responses evolve. Existing methods personalize corrective messages or broadcast fact-checks, but rarely treat changing social responses as feedback for subsequent intervention. We address this limitation by formulating socially driven post-exposure intervention, in which observable user responses guide where and how to intervene over time. We represent local social groups as hyperedges in a social hypergraph, coupling each group's evolving discourse with its intervention audience while preserving users shared across groups. Within an LLM-agent simulation, an external Intervention Agent identifies risk hyperedges, generates evidence-grounded group corrections, coordinates messages for shared users, and uses previous intervention outcomes to inform later rounds. Across four misinformation datasets, our framework achieves the lowest simulated Trustamong all compared methods, reducing it by 56%–58% relative to no intervention. It also reduces SpreadRate by 49%–84%, although its advantage over competing interventions varies across datasets.