D-MODD is a data-derived Langevin stochastic differential equation whose transition kernel reproduces the one-step opinion change probabilities observed in social media data on a polarized climate topic.
arXiv preprint arXiv:2201.01322 , year=
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Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
A framework uses stance detection, linear dimensionality reduction, and neural potential landscapes to recover a 3D stance space explaining 45% variance and to visualize large-scale shifts across platforms and years.
Simulations of a modified Axelrod model on scale-free networks with continuous opinions reveal polarization trends, with empathetic agents showing limited success unless highly connected agents alter their behavior.
citing papers explorer
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D-MODD: A Diffusion Model of Opinion Dynamics Derived from Online Data
D-MODD is a data-derived Langevin stochastic differential equation whose transition kernel reproduces the one-step opinion change probabilities observed in social media data on a polarized climate topic.
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
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Mapping the Winds of Stance Dynamics using Potential Landscape Models
A framework uses stance detection, linear dimensionality reduction, and neural potential landscapes to recover a 3D stance space explaining 45% variance and to visualize large-scale shifts across platforms and years.
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Modified Axelrod Model Showing Opinion Convergence And Polarization In Realistic Scale-Free Networks
Simulations of a modified Axelrod model on scale-free networks with continuous opinions reveal polarization trends, with empathetic agents showing limited success unless highly connected agents alter their behavior.