Understanding Political Divisiveness using Online Participation data from the 2022 French and Brazilian Presidential Elections

TL;DR

This paper introduces divisiveness metrics derived from online participation data during the 2022 French and Brazilian presidential elections, offering new tools to identify polarizing proposals and understand political divides.

Contribution

It presents novel divisiveness metrics based on online participation data that complement traditional social choice aggregation functions, without requiring demographic data.

Findings

Divisiveness metrics identify polarizing proposals effectively.

Metrics explain issues that divide populations.

Divisiveness scores are estimable without demographic data.

Abstract

Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programs by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.