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Digital technologies can augment civic participation by facilitating theexpression of detailed political preferences. Yet, digital participationefforts often rely on methods optimized for elections involving a fewcandidates. Here we present data collected in an online experiment whereparticipants built personalized government programs by combining policiesproposed by the candidates of the 2022 French and Brazilian presidentialelections. We use this data to explore aggregates complementing those used insocial choice theory, finding that a metric of divisiveness, which isuncorrelated with traditional aggregation functions, can identify polarizingproposals. These metrics provide a score for the divisiveness of each proposalthat can be estimated in the absence of data on the demographic characteristicsof participants and that explains the issues that divide a population. Thesefindings suggest divisiveness metrics can be useful complements to traditionalaggregation functions in direct forms of digital participation.

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