Voter model can accurately predict individual opinions in online populations

Abstract

Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate general opinion distributions observed in real-world scenarios, their capacity to align with data at the user level remains mostly untested. We evaluate the capacity of the multi-state voter model with zealots to capture individual opinions in a fine-grained Twitter dataset collected during the 2017 French Presidential elections. Our findings reveal a strong correspondence between individual opinion distributions in the equilibrium state of the model and ground-truth political leanings of the users. Additionally, we demonstrate that discord probabilities accurately identify pairs of like-minded users. These results emphasize the validity of the voter model in complex settings, and advocate for further empirical evaluations of opinion dynamics models at the user level.

References in corpus (13)

• On the Role of Zealotry in the Voter Model
• Analytical Solution of the Voter Model on Disordered Networks
• Emergence of polarized ideological opinions in multidimensional topic spaces
• Emergence of polarization in coevolving networks
• Modeling Explosive Opinion Depolarization in Interdependent Topics
• Forecasting elections results via the voter model with stubborn nodes
• Multidimensional political polarization in online social networks
• Filter Bubble effect in the multistate voter model
• Local and global ordering dynamics in multi-state voter models
• Partisan Voter Model: Stochastic description and noise-induced transitions
• Echo chamber effects in signed networks
• Polarized opinion states in static networks driven by limited information horizons
• Discord in the voter model for complex networks

Cited by in corpus (1)

• Competition between private and expressed opinions in binary choice: the $\alpha$-EPO $q$-voter model