Inferring the microscopic mechanisms of opinion dynamics using a kinetic Ising model
Abstract
Kinetic Ising models are widely used to describe binary opinion dynamics, but their microscopic validity has rarely been tested empirically. Here, we infer the transition probabilities governing opinion updates from a year-long online social network and show that they are accurately described by an Ising heat-bath dynamics. The inferred parameters admit a direct sociological interpretation: the external field quantifies intrinsic bias, the coupling strength measures social influence, and a persistence term captures temporal inertia. We further show that persistence is positively correlated with node degree, while both persistence and interaction strength are strongly correlated with global network heterogeneity and clustering. Using the inferred time-dependent parameters in Monte Carlo simulations on the empirical temporal networks, we accurately reproduce the observed response functions, flip probabilities, and macroscopic opinion dynamics. Within the Twitter climate debate, our results provide direct empirical support for a kinetic Ising description of online opinion formation and establish a quantitative link between microscopic social behavior and evolving network topology.
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BibTeX
@misc{achitouv2026inferring,
title = {{Inferring the microscopic mechanisms of opinion dynamics using a kinetic Ising model}},
author = {Ixandra Achitouv and David Chavalarias and Vincent Lahoche},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.23690},
eprint = {2609.23690},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05756114v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Inferring the microscopic mechanisms of opinion dynamics using a kinetic Ising model
AU - Ixandra Achitouv
AU - David Chavalarias
AU - Vincent Lahoche
PY - 2026
DA - 2026/09/20
PB - HAL
DO - 10.48550/arxiv.2609.23690
UR - https://hal.science/hal-05756114v1
AB - Kinetic Ising models are widely used to describe binary opinion dynamics, but their microscopic validity has rarely been tested empirically. Here, we infer the transition probabilities governing opinion updates from a year-long online social network and show that they are accurately described by an Ising heat-bath dynamics. The inferred parameters admit a direct sociological interpretation: the external field quantifies intrinsic bias, the coupling strength measures social influence, and a persistence term captures temporal inertia. We further show that persistence is positively correlated with node degree, while both persistence and interaction strength are strongly correlated with global network heterogeneity and clustering. Using the inferred time-dependent parameters in Monte Carlo simulations on the empirical temporal networks, we accurately reproduce the observed response functions, flip probabilities, and macroscopic opinion dynamics. Within the Twitter climate debate, our results provide direct empirical support for a kinetic Ising description of online opinion formation and establish a quantitative link between microscopic social behavior and evolving network topology.
ER -