Positive algorithmic bias cannot stop fragmentation in homophilic networks

Abstract

Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and non-academic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process. Furthermore, we derive that no level of positive algorithmic bias in the form of rewiring is capable of preventing fragmentation and its effect on reducing the fragmentation speed is negligible.

Cite as: Chris Blex & Taha Yasseri (2020) Positive algorithmic bias cannot stop fragmentation in homophilic networks, The Journal of Mathematical Sociology, DOI: 10.1080/0022250X.2020.1818078

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• Modularity and community structure in networks
• Ising, Schelling and Self-Organising Segregation
• Statistical physics of the Schelling model of segregation
• Understanding Filter Bubbles and Polarization in Social Networks

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