# IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics

arXiv: https://arxiv.org/abs/2604.06600 · HTML: https://mlsanity.com/paper/2604.06600/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2026.

## Built on by (most cited first, 11 of 11)

- [Rethinking Layer-Wise Information Allocation for Vision Foundation Model Adaptation](https://mlsanity.com/paper/2607.21973/) (2026)
- [Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction](https://mlsanity.com/paper/2604.20311/) (2026)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [CurEvo: Curriculum-Guided Self-Evolution for Video Understanding](https://mlsanity.com/paper/2604.26707/) (2026)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse](https://mlsanity.com/paper/2608.10810/) (2026)
- [GateMOT: Q-Gated Attention for Dense Object Tracking](https://mlsanity.com/paper/2604.26353/) (2026)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Don' t Box Me In: Dynamic Cultural Adaptation and Cognitive Tracking for Social Understanding](https://mlsanity.com/paper/2608.22411/) (2026)
- [LLMs for Social Network Modeling: From Network Generation to Dynamic Processes](https://mlsanity.com/paper/2609.08049/) (2026)
- [Too Good to Be Real? Diagnosing and Reducing the Gap Between AI Preference and Real User Engagement](https://mlsanity.com/paper/2609.18282/) (2026)

## Abstract (arXiv)

LLM-based social network simulation introduces a new computational approach for modeling event evolution in complex online environments. However, existing methods typically simulate social processes under a fixed event trajectory, treating the event as static once initialized and overlooking intervention dynamics, and thus fail to capture the intrinsic evolution of real social network events, where source-side interventions and collective interactions continuously reshape event trajectories, sometimes leading to secondary popularity explosions and collective attitude shifts. To address this limitation, we introduce an intervention-aware simulation framework, IntervenSim, that models event evolution and intervention in a closed loop. We model event developments and source-side interventions using source agents, and collective crowd reactions using crowd agents, capturing their continuous co-evolution through an intervention-aware mechanism that couples source-side intervention, group interaction, and feedback-driven adjustment of subsequent interventions. Experiments on diverse real-world events show that IntervenSim improves MAPE by 41.6% and DTW by 66.9% over prior frameworks, while reducing computational cost with fewer yet more capable agents. These improvements indicate that IntervenSim not only simulates regular event trajectories more faithfully, but also better captures opinion dynamics under intervention in complex cases.