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

• URL: http://arxiv.org/abs/2601.16226v1
• Date: Fri, 16 Jan 2026 16:17:44 GMT
• Title: D-MODD: A Diffusion Model of Opinion Dynamics Derived from Online Data
• Authors: Ixandra Achitouv, David Chavalarias,
• Abstract summary: We present the first empirical derivation of a continuous-time model for real-world opinion dynamics.<n>We show that the observed dynamics are well described by a Langevin-type differential equation.<n>Our results provide the first direct evidence that online opinion dynamics on a polarized topic admit a Markovian description at the operator level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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