IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics
This addresses the limitation of existing LLM-based social network simulations that overlook intervention dynamics, offering a more accurate tool for researchers and practitioners studying online event evolution.
The paper tackles the problem of simulating opinion dynamics in social networks by introducing an intervention-aware framework, IntervenSim, which models event evolution and interventions in a closed loop, resulting in improvements of 41.6% in MAPE and 66.9% in DTW over prior methods.
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.