D-MODD: A Diffusion Model of Opinion Dynamics Derived from Online Data

TL;DR

This paper empirically derives a continuous-time stochastic model for opinion dynamics using social media data, revealing that online opinions follow a Langevin-type stochastic differential equation with attractor basins.

Contribution

It introduces the first data-driven Langevin model for real-world opinion dynamics, linking sociophysics, behavioral data, and complex systems modeling.

Findings

Empirically reconstructed transition probabilities match the model's predictions.

Opinion dynamics are well described by a Langevin-type stochastic differential equation.

The model reveals persistent attractor basins in opinion space.

Abstract

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.