D-MODD: A Diffusion Model of Opinion Dynamics Derived from Online Data
This work provides the first empirical evidence that online opinion dynamics on polarized topics can be modeled as a Markovian process at the operator level, bridging sociophysics and data-driven modeling.
The authors derive a continuous-time stochastic model (D-MODD) for opinion dynamics from longitudinal social-media data on a climate-change topic, showing that the dynamics follow a Langevin-type SDE with attractor basins and that the model's transition probabilities closely match empirical data.
We present the first empirical derivation of a continuous-time stochastic model for real-world opinion dynamics. Using longitudinal social-media data to infer users opinion on a binary climate-change topic, we reconstruct the underlying drift and diffusion functions governing individual opinion updates. We show that the observed dynamics are well described by a Langevin-type stochastic differential equation, with persistent attractor basins and spatially sensitive drift and diffusion terms. The empirically inferred one-step transition probabilities closely reproduce the transition kernel generated from the D-MODD model we introduce. Our results provide the first direct evidence that online opinion dynamics on a polarized topic admit a Markovian description at the operator level, with empirically reconstructed transition kernels accurately reproduced by a data-driven Langevin model, bridging sociophysics, behavioral data, and complex-systems modeling.