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Algorithmic Amplification of Politics and Engagement Maximization on Social Media

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Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1144))

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Abstract

This study examines how engagement-maximizing recommender systems influence the visibility of Members of Parliament’s tweets in timelines. Leveraging engagement predictive models and Twitter data, we evaluate various recommender systems. Our analysis reveals that prioritizing engagement decreases the ideological diversity of the audiences reached by Members of Parliament and increases the reach disparities between political groups. When evaluating the algorithmic amplification within the general population, engagement-based timelines confer greater advantages to mainstream right-wing parties compared to their left-wing counterparts. However, when considering users’ individual political leanings, engagement-based timelines amplify ideologically aligned content. We stress the need for audits accounting for user characteristics when assessing the distortions introduced by personalization algorithms and advocate addressing online platform regulations by directly evaluating the metrics platforms aim to optimize, beyond the mere algorithmic implementation.

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Acknowledgments

The author deeply thanks Pedro Ramaciotti Morales for his precious insights and Mazyiar Panahi for enabling the collection of the large-scale retweet network. Finally, the author acknowledges the Jean-Pierre Aguilar fellowship from the CFM Foundation for Research, the support and resources provided by the Complex Systems Institute of Paris Île-de-France.

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Authors and Affiliations

  1. École des Hautes Études En Sciences Sociales, Paris, France

    Paul Bouchaud

  2. Institut des Systèmes Complexes Paris Île-de-France, Paris, France

    Paul Bouchaud

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  1. Paul Bouchaud
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Correspondence to Paul Bouchaud .

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Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

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Bouchaud, P. (2024). Algorithmic Amplification of Politics and Engagement Maximization on Social Media. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-53503-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53502-4

  • Online ISBN: 978-3-031-53503-1

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