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3.3 Link recommendation algorithms and dynamics of polarization in online social networks

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

Keywords: Polarization; social networks; complex systems; link recommendation; opinion dynamics.

Authors on the article: 

Online social media platforms are nowadays spaces where political opinions are formed, reinforced, and confronted. These platforms are also environments where humans co-exist with algorithms [1]. Understanding opinion dynamics thus requires comprehending the interrelated subtleties of human decision-making and the outcomes of automated decisions. As high levels of political polarization raise concerns in different parts of the world, we should ask: What is the impact of social network algorithms in this process? Can such algorithms be used as an intervention mechanism to control polarization? 

In a recent paper published in the Proceedings of the National Academy of Sciences (special feature on Dynamics of Political Polarization) we provide a complex adaptive systems perspective on the effects of link recommendation algorithms in opinion polarization. These algorithms — also called user, contact, or people recommender systems— are used to recommend new connections to users [2-4]. We show that link recommendation algorithms that suggest connections to be established between users already sharing many “friends” — as it is likely to be the case in current applications — lead tight-knit communities to emerge. This exacerbates polarization as isolated communities of users can perpetuate diverging opinions and individuals are less likely to be exposed to a diverse pool of viewpoints. 

To study the dynamics of link recommendations and opinion formation, we develop a new computational model to simulate the co-evolution of opinions [5-6] on a dynamic social network. This model allows us to test how tuning the dependence of link recommendations on the number of common friends might impact the social network topology and, in turn, impact opinion polarization. 

Our study is relevant for three key reasons: first, it sheds light on the impacts of social-network algorithms on dynamics of polarization and consensus; second, it suggests that small modifications in how recommendations are made can significantly impact information flows and opinion polarization; finally, and more broadly, it reveals that understanding the impacts of algorithms in our society requires framing their effects in the context of complex adaptive systems where individuals and automated decisions (co-)adapt to each other over time. 

Overall, this study stresses the impacts of social-network algorithms and unveils avenues to control dynamics of radicalization and polarization on online social networks. We hope this inspires an interdisciplinary audience (as the readership of the IAS Review) of computer scientists, psychologists, political scientists, and policymakers to recognize the potential impacts of social network algorithms in long-term behaviour and political polarization. While our article explores a process of link formation based on common friends, we invite new theoretical and empirical analysis that investigate how more complex link recommendations might help to establish connections that are both useful for users, likely to be followed, and instrumental to curbing polarization on social media. 

Further Readings Suggestions / References: 

[1] Woolley, S. C., & Howard, P. N. (2016). Political communication, computational propaganda, and autonomous agents: Introduction. International journal of Communication 
[2] Terveen, L., & McDonald, D. W. (2005). Social matching: A framework and research agenda. ACM transactions on computer-human interaction. 
[3] Chen, J., Geyer, W., Dugan, C., Muller, M., & Guy, I. (2009). Make new friends, but keep the old: recommending people on social networking sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 
[4] Daly, E. M., Geyer, W., & Millen, D. R. (2010). The network effects of recommending social connections. In Proceedings of the fourth ACM conference on Recommender Systems 
[5] Baumann, F., Lorenz-Spreen, P., Sokolov, I. M., & Starnini, M. (2020). Modeling echo chambers and polarization dynamics in social networks. Physical Review Letters 
[6] Vasconcelos, V. V., Levin, S. A., & Pinheiro, F. L. (2019). Consensus and polarization in competing complex contagion processes. Journal of the Royal Society Interface 

Santos, F.P., Lelkes, Y., Levin, S.A. (2021) Link recommendation algorithms and dynamics of polarization in online social networks. Proceedings of the National Academy of Sciences of the United States of America, 118 (50) e2102141118.