Kinetic Ising model explains online opinion changes on twitter
Inferring the microscopic mechanisms of opinion dynamics using a kinetic Ising model
Computers and SocietySocial and Information Networks
Summary
The paper looks at how people’s opinions change over time on social networks like Twitter. The authors use a physics-inspired model called the kinetic Ising model to describe how individuals update their yes-or-no opinions based on social influence, personal bias, and past opinions. They find that this model matches real opinion changes in online debates about climate. This approach links individual behavior with how the social network changes, helping us understand online discussions better.
What this means in practice
- •For social media analysts: Model how users' binary opinions evolve over time on platforms like Twitter accounting for social influence and personal bias.
- •For social network platform engineers: Simulate realistic opinion spread on temporal networks to test features affecting online discussions and user engagement.
Tested on one dataset.
Authors
Ixandra Achitouv, David Chavalarias, Vincent Lahoche
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.