  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.
