Modelling election dynamics and the impact of disinformation
arXiv:1904.12614 · doi:10.1007/s41884-019-00021-2
Abstract
Complex dynamical systems driven by the unravelling of information can be modelled effectively by treating the underlying flow of information as the model input. Complicated dynamical behaviour of the system is then derived as an output. Such an information-based approach is in sharp contrast to the conventional mathematical modelling of information-driven systems whereby one attempts to come up with essentially {\it ad hoc} models for the outputs. Here, dynamics of electoral competition is modelled by the specification of the flow of information relevant to election. The seemingly random evolution of the election poll statistics are then derived as model outputs, which in turn are used to study election prediction, impact of disinformation, and the optimal strategy for information management in an election campaign.
arXiv comment20 pages, 5 figures
References in corpus (9)
- Influence of fake news in Twitter during the 2016 US presidential election
- Fake News Detection on Social Media: A Data Mining Perspective
- TI-CNN: Convolutional Neural Networks for Fake News Detection
- Information geometry in vapour-liquid equilibrium
- Dam Rain and Cumulative Gain
- Quantum noise and stochastic reduction
- Characterizing Political Fake News in Twitter by its Meta-Data
- A Bayesian Model for False Information Belief Impact, Optimal Design, and Fake News Containment
- On the Pricing of Storable Commodities