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Journal Articles Applied Network Science Year : 2021

Forecasting elections results via the voter model with stubborn nodes

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Abstract

We explore a method to influence or even control the diversity of opinions within a polarised social group. We leverage the voter model in which users hold binary opinions and repeatedly update their beliefs based on others they connect with. Stubborn agents who never change their minds (\zealots") are also disseminated through the network, which is modelled by a connected graph. Building on earlier results, we provide a closed-form expression for the average opinion of the group at equilibrium. This leads us to a strategy to inject zealots into a polarised network in order to shift the average opinion towards any target value. We account for the possible presence of a backfire effect, which may lead the group to react negatively and reinforce its level of polarisation in response. Our results are supported by numerical experiments on synthetic data.
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Dates and versions

hal-02946434 , version 1 (23-09-2020)
hal-02946434 , version 2 (13-10-2021)

Identifiers

Cite

Antoine Vendeville, Benjamin Guedj, Shi Zhou. Forecasting elections results via the voter model with stubborn nodes. Applied Network Science, 2021. ⟨hal-02946434v2⟩
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