Link recommendation algorithms and dynamics of polarization in online social networks

Fernando P. Santos, Yphtach Lelkes and Simon A. Levin
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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;

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 50, e2102141118

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.

Keywords: polarization; social networks; complex systems; link recommendation; opinion dynamics (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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