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Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model

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  • Alina Sîrbu
  • Dino Pedreschi
  • Fosca Giannotti
  • János Kertész

Abstract

The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.

Suggested Citation

  • Alina Sîrbu & Dino Pedreschi & Fosca Giannotti & János Kertész, 2019. "Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-20, March.
    • Handle: RePEc:plo:pone00:0213246
    DOI: 10.1371/journal.pone.0213246
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    References listed on IDEAS

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    1. Clio Andris & David Lee & Marcus J Hamilton & Mauro Martino & Christian E Gunning & John Armistead Selden, 2015. "The Rise of Partisanship and Super-Cooperators in the U.S. House of Representatives," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
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    Cited by:

    1. Markus Brede, 2019. "How Does Active Participation Affect Consensus: Adaptive Network Model of Opinion Dynamics and Influence Maximizing Rewiring," Complexity, Hindawi, vol. 2019, pages 1-16, June.
    2. Anson Au, 2023. "A Theoretical Examination of Homophily Beyond Focus Theory: Causes, Consequences, and New Directions," SAGE Open, , vol. 13(2), pages 21582440231, May.
    3. Goonj Mohan, 2024. "The Data Economy and Polarization on Social Media," UB School of Economics Working Papers 2024/462, University of Barcelona School of Economics.
    4. Kathie M. d'I. Treen & Hywel T. P. Williams & Saffron J. O'Neill, 2020. "Online misinformation about climate change," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.
    5. Tinggui Chen & Qianqian Li & Jianjun Yang & Guodong Cong & Gongfa Li, 2019. "Modeling of the Public Opinion Polarization Process with the Considerations of Individual Heterogeneity and Dynamic Conformity," Mathematics, MDPI, vol. 7(10), pages 1-33, October.
    6. Borges, Henrique M. & Vasconcelos, Vítor V. & Pinheiro, Flávio L., 2024. "How social rewiring preferences bridge polarized communities," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    7. Wenlong Sun & Olfa Nasraoui & Patrick Shafto, 2020. "Evolution and impact of bias in human and machine learning algorithm interaction," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.

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