Emergent dynamics of extremes in a population driven by common information sources and new 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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1 Scopus citations

Abstract

We quantify how and when extreme subpopulations emerge in a model society despite everyone having the same information and available resources – and show that counterintuitively these extremes will likely be enhanced over time by new social media algorithms designed to reduce division. We verify our analysis mathematically, and show it reproduces (a) the time-dependent behavior observed in controlled experiments on humans, (b) the findings of a recent study of online behavior by Facebook concerning the impact of ‘soft’ and ‘hard’ news, (c) the observed temporal emergence of extremes in U.S. House of Representatives voting, and (d) the real-time emergence of a division in national opinion during the ongoing peace process in Colombia. We uncover a novel societal tipping point which is a ‘ghost’ of a nearby saddle-node bifurcation from dynamical systems theory, and which provides a novel policy opportunity for preventing extremes from emerging.

Original languageEnglish (US)
Article number11895
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • General

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    Johnson, N. F., Manrique, P., Zheng, M., Cao, Z., Botero, J., Huang, S., Aden, N., Song, C., Leady, J., Velasquez, N., & Restrepo, E. M. (2019). Emergent dynamics of extremes in a population driven by common information sources and new social media algorithms. Scientific reports, 9(1), [11895]. https://doi.org/10.1038/s41598-019-48412-w