A data-driven kinetic model for opinion dynamics with social network contacts
arXiv:2307.00906 · doi:10.1017/S0956792524000068
Abstract
Opinion dynamics is an important and very active area of research that delves into the complex processes through which individuals form and modify their opinions within a social context. The ability to comprehend and unravel the mechanisms that drive opinion formation is of great significance for predicting a wide range of social phenomena such as political polarization, the diffusion of misinformation, the formation of public consensus, and the emergence of collective behaviors. In this paper, we aim to contribute to that field by introducing a novel mathematical model that specifically accounts for the influence of social media networks on opinion dynamics. With the rise of platforms such as Twitter, Facebook, and Instagram and many others, social networks have become significant arenas where opinions are shared, discussed, and potentially altered. To this aim after an analytical construction of our new model and through incorporation of real-life data from Twitter, we calibrate the model parameters to accurately reflect the dynamics that unfold in social media, showing in particular the role played by the so-called influencers in driving individual opinions towards predetermined directions.
References in corpus (14)
- On a kinetic model for a simple market economy
- Kinetic Exchange Models for Income and Wealth Distributions
- Wealth distribution and collective knowledge. A Boltzmann approach
- Kinetic models for optimal control of wealth inequalities
- Optimal control of epidemic spreading in presence of social heterogeneity
- Spatial spread of COVID-19 outbreak in Italy using multiscale kinetic transport equations with uncertainty
- Kinetic modeling of alcohol consumption
- Fundamental diagrams in traffic flow: the case of heterogeneous kinetic models
- Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning
- Spreading of fake news, competence, and learning: kinetic modeling and numerical approximation
- The Aw-Rascle traffic model: Enskog-type kinetic derivation and generalisations
- Kinetic description of collision avoidance in pedestrian crowds by sidestepping
- Kinetic derivation of Aw-Rascle-Zhang-type traffic models with driver-assist vehicles
- Data assimilation in price formation