Latent Space Modeling of Multidimensional Networks with Application to the Exchange of Votes in Eurovision Song Contest

TL;DR

This paper develops a Bayesian latent space model to analyze voting patterns in Eurovision, revealing underlying relationships among countries and evidence of strategic bias beyond geographical proximity.

Contribution

It introduces a novel latent space modeling approach for multivariate networks, specifically applied to Eurovision voting data, to uncover latent structures influencing voting behavior.

Findings

Latent space positions partially align with geographical locations.

The model detects strategic voting biases.

Proximity in latent space influences voting likelihood.

Abstract

The Eurovision Song Contest is a popular TV singing competition held annually among country members of the European Broadcasting Union. In this competition, each member can be both contestant and jury, as it can participate with a song and/or vote for other countries' tunes. Throughout the years, the voting system has repeatedly been accused of being biased by the presence of tactical voting, according to which votes would represent strategic interests rather than actual musical preferences of the voting countries. In this work, we develop a latent space model to investigate the presence of a latent structure underlying the exchange of votes. Focusing on the period from 1998 to 2015, we represent the vote exchange as a multivariate network: each edition is a network, where countries are the nodes and two countries are linked by an edge if one voted for the other. The different networks are taken to be independent replicates of a common latent space capturing the overall relationships among the countries. Proximity denotes similarity, and countries close in the latent space are assumed to be more likely to exchange votes. Therefore, if the exchange of votes depends on the similarity between countries, the quality of the competing songs might not be a relevant factor in the determination of the voting preferences, and this would suggest the presence of bias. A Bayesian hierarchical modelling approach is employed to model the probability of a connection between any two countries as a function of their distance in the latent space, and of network-specific parameters and edge-specific covariates. The inferred latent space is found to be relevant in the determination of edge probabilities, however, the positions of the countries in such space only partially correspond to their actual geographical positions.