Center for Collective Learning

Latest project – Understanding political divisiveness using online participation data from the 2022 French and Brazilian presidential elections

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 programmes 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 that divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.

During the research, the authors used primary data collected during the French and Brazilian elections using an online platform. The participants in the anonymous experiment stated that – according to their own opinion – they consider themselves to be supporters of the political right or the left, and also stated the extent to which they support each item from the program of the starting parties (e.g. introduction of free school meals, LGBTQ+ rights, etc.)

The authors examined the received data from several points of view, e.g. what are the connections between the supported causes and the demographic group of the voters.