What is it about?
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 recommending new connections between users. We show that preferentially establishing links between people sharing many friends — as often suggested by some link recommendation algorithms — potentiates opinion polarization by creating network topologies with isolated communities. This happens as isolated groups are more likely to sustain diverging opinions and lead individuals to be less exposed to a diverse pool of viewpoints. By contrast, preferentially linking nodes with few common connections has the potential to moderate opinions.
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Why is it important?
The long-term effects of algorithmic recommendations, here concerning opinion formation, are often hard to anticipate. In this context, our study is relevant for three key reasons: first, it sheds light on the impacts of social-network algorithms in dynamics of polarization and consensus; second, it suggests that small modifications in how recommendations are made can have important impacts in forming polarized opinions; 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 (co-)adapt to each other over time.
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This page is a summary of: Link recommendation algorithms and dynamics of polarization in online social networks, Proceedings of the National Academy of Sciences, December 2021, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2102141118.
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