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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
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    Citations

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    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. Beacham, Austin & Hafner-Burton, Emilie M & Schneider, Christina J, 2024. "The Weaponization of Information Technologies and Democratic Resilience," Institute on Global Conflict and Cooperation, Working Paper Series qt6f24q81x, Institute on Global Conflict and Cooperation, University of California.
    8. Borges, Henrique M. & Vasconcelos, Vítor V. & Pinheiro, Flávio L., 2024. "How social rewiring preferences bridge polarized communities," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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