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

Nature Human Behaviour volume 8, pages 137–148 (2024)Cite this article

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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 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.

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Fig. 1: Participation platform and pairwise comparison data.
Fig. 2: Understanding participants' preferences.
Fig. 3: Divisiveness analysis.
Fig. 4: Axiomatic and matrixial properties of agreement and divisiveness.
Fig. 5: Reproducing key results in two Strict Order Complete (SOC) datasets stored on Preflib.org.

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Data availability

The datasets collected during the current study are deposited in Harvard Dataverse at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8E0EA4. The datasets used for validation of our metric of divisiveness are publicly available on Preflib.org and can be found at https://www.preflib.org/dataset/00014 and https://www.preflib.org/dataset/00006.

Code availability

Data were collected via two digital democracy systems released in Brazil and France preceding their respective 2022 Presidential Elections. The code used to create these platforms is available at https://github.com/CenterForCollectiveLearning/opencracia. Data analysis was conducted using Python (v.3.10.8), and regression analysis was conducted using R (v.4.2.1). Our pipeline includes the use of Pandas (v.1.5.1), NumPy (v.1.23.4) and SciPy (v.1.9.3). The algorithms for implementing divisiveness and aggregation functions are publicly available on the Comchoice library at https://github.com/CenterForCollectiveLearning/comchoice/. To further validate our metric of divisiveness, we also collected third-party data from Preflib.org, a comprehensive resource maintained by the Computational Social Choice community.

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Acknowledgements

This project was supported by the Artificial and Natural Intelligence Toulouse Institute – 3IA Institute: ANR-19-PI3A-0004, the French National Research Agency (ANR) under grant ANR-17-EURE-0010 (Investissements d’Avenir programme), the EUROPEAN RESEARCH EXECUTIVE AGENCY (REA) (https://doi.org/10.3030/101086712), and by the European Lighthouse of AI for Sustainability, HORIZON-CL4-2022-HUMAN-02 project ID: 101120237. The work of U.G. and R.C. were supported by ANR JCJC project SCONE (ANR 18-CE23-0009-01). J.L.’s work was funded in part by the French government under management of Agence Nationale de la Recherche as part of the ‘Investissements d’avenir’ programme, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge the graphic design support of A. Nahhal for the creation of the MonProgramme platform.

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Authors and Affiliations

  1. Center for Collective Learning, ANITI, TSE, IAST, IRIT, Université de Toulouse, Toulouse, France

    Carlos Navarrete, Mariana Macedo, Jingling Zhang, Nicole Ferrada & César A. Hidalgo

  2. IRIT, Université Toulouse Capitole, Toulouse, France

    Rachael Colley & Umberto Grandi

  3. Sociology Department, Federal University of Pernambuco, Recife, Pernambuco, Brazil

    Maria Eduarda Mello

  4. Computer Engineering Department, University of Pernambuco, Recife, Pernambuco, Brazil

    Rodrigo Lira & Carmelo Bastos-Filho

  5. LAMSADE, CNRS, Université Paris-Dauphine, PSL, Paris, France

    Jérôme Lang

  6. Alliance Manchester Business School, University of Manchester, Manchester, UK

    César A. Hidalgo

  7. Center for Collective Learning, CIAS, Corvinus University, Budapest, Hungary

    César A. Hidalgo

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  1. Carlos Navarrete
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Contributions

C.N. and C.A.H. contributed to the study conception and design, acquisition of data, data analysis, interpretation of data and drafting of the paper. U.G., J.L., R.C., M.M., N.F., R.L., C.B-F., M.E.M. and J.Z. participated in the creation and diffusion of the platforms and provided comments to improve the paper.

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Correspondence to César A. Hidalgo.

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Navarrete, C., Macedo, M., Colley, R. et al. Understanding political divisiveness using online participation data from the 2022 French and Brazilian presidential elections. Nat Hum Behav 8, 137–148 (2024). https://doi.org/10.1038/s41562-023-01755-x

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