Forecasting elections results via the voter model with stubborn nodes

Antoine Vendeville (1, 2) , Benjamin Guedj (3, 1, 4, 5, 2) , Shi Zhou (1)

1 UCL-CS - Department of Computer science [University College of London]
2 Inria-London - The Inria London Programme
3 UCL - University College of London [London]
4 Inria-CWI - Inria-CWI
5 MODAL - MOdel for Data Analysis and Learning

In this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and uses theoretical results developed in a previous work of ours. We look at popular vote shares for the Conservative and Labour parties in the UK and the Republican and Democrat parties in the US. We are able to perform time-evolving estimates of the model parameters and use these to forecast the vote shares for each party in any election. We obtain a mean absolute error of 4.74%. As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the quantity of voters that are strongly pro and against the considered parties.

Mots clés: Elections; voter model; opinion dynamics; Markov chains; social networks

Domaines: Réseaux sociaux et d'information [cs.SI]; Apprentissage [cs.LG]

Antoine Vendeville, Benjamin Guedj, Shi Zhou. Forecasting elections results via the voter model with stubborn nodes. 2020. ⟨hal-02946434v1⟩