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arXiv:1712.06009physics.soc-ph

Population polarization dynamics and next-generation social media algorithms

N. F. Johnson, P. Manrique, M. Zheng, Z. Cao, J. Botero, S. Huang, N. Aden, C. Song, J. Leady, N. Velasquez, E. M. Restrepo

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Abstract

We present a many-body theory that explains and reproduces recent observations of population polarization dynamics, is supported by controlled human experiments, and addresses the controversy surrounding the Internet's impact. It predicts that whether and how a population becomes polarized is dictated by the nature of the underlying competition, rather than the validity of the information that individuals receive or their online bubbles. Building on this framework, we show that next-generation social media algorithms aimed at pulling people together, will instead likely lead to an explosive percolation process that generates new pockets of extremes.

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DOI
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physics.soc-phPhysics and Society nlin.AOAdaptation and Self-Organizing Systems

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Apr 2018 to Apr 2018. Found in 1 HN submission from Apr 2018 to Apr 2018. Open submission discussions.

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    • I think it's because we don't understand systems as well as we should. This article (http://www.miamiherald.com/news/business/article207841309.ht...) is a decent intro to what modeling shows about engagement and polarization. The paper it refers to…

      michaelfeathersView on HNParent context

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