IDEAS home Printed from https://ideas.repec.org/a/nas/journl/v118y2021pe2102141118.html
   My bibliography  Save this article

Link recommendation algorithms and dynamics of polarization in online social networks

Author

Listed:
  • Fernando P. Santos

    (a Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544;; b Informatics Institute, University of Amsterdam,1098XH Amsterdam, The Netherlands;)

  • Yphtach Lelkes

    (c Annenberg School for Communication Research, University of Pennsylvania, Philadelphia, PA 19104)

  • Simon A. Levin

    (a Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544;)

Abstract

Polarization is rising while political debates are moving to online social platforms. In such settings, algorithms are used to recommend new connections to users, through so-called link recommendation algorithms. Users are often recommended based on structural similarity (e.g., nodes sharing many neighbors are similar). We show that preferentially establishing links with structurally similar nodes potentiates opinion polarization by stimulating network topologies with well-defined communities (even in the absence of opinion-based rewiring). When networks are composed of nodes that react differently to out-group contacts—either converging or polarizing—connecting structurally dissimilar nodes enhances moderate opinions. Our study sheds light on the impacts of social-network algorithms in opinion dynamics and unveils avenues to steer polarization in online social networks.

Suggested Citation

  • Fernando P. Santos & Yphtach Lelkes & Simon A. Levin, 2021. "Link recommendation algorithms and dynamics of polarization in online social networks," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(50), pages 2102141118-, December.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2102141118
    as

    Download full text from publisher

    File URL: http://www.pnas.org/content/118/50/e2102141118.full
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peng Liu & Liang Gui & Huirong Wang & Muhammad Riaz, 2022. "A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    2. Ni, Xuelian & Xiong, Fei & Pan, Shirui & Chen, Hongshu & Wu, Jia & Wang, Liang, 2023. "How heterogeneous social influence acts on human decision-making in online social networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    3. Cui, Peng-Bi, 2023. "Exploring the foundation of social diversity and coherence with a novel attraction–repulsion model framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    4. Pérez-Martínez, H. & Bauzá Mingueza, F. & Soriano-Paños, D. & Gómez-Gardeñes, J. & Floría, L.M., 2023. "Polarized opinion states in static networks driven by limited information horizons," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    5. Di Benedetto, Andrea & Wieners, Claudia E. & Dijkstra, Henk A. & Stoof, Henk T.C., 2023. "Media preference increases polarization in an agent-based election model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    6. Cano Macias, Ricardo & Ruiz Vera, Jorge Mauricio, 2024. "Dynamics of opinion polarization in a population," Mathematical Social Sciences, Elsevier, vol. 128(C), pages 31-40.
    7. Borges, Henrique M. & Vasconcelos, Vítor V. & Pinheiro, Flávio L., 2024. "How social rewiring preferences bridge polarized communities," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nas:journl:v:118:y:2021:p:e2102141118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Eric Cain (email available below). General contact details of provider: http://www.pnas.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.