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.

KEYWORDS:

• Algorithmic bias
• echo chambers
• homophily
• Schelling model
• social networks

Acknowledgments

The authors would like to thank Oliver M. Crook for his advice on using Euler–Lagrange equations for finding the minimal algorithmic bias. The authors would also like to thank Renaud Lambiotte for his advice.

Declaration of interest statement

No financial interest or benefit has arisen from the direct applications of this research.

Additional information

Funding

Chris Blex was funded by the Alan Turing Institute Studentship. Taha Yasseri was partially supported by The Alan Turing Institute under the EPSRC grant [EP/N510129/1]. The Funder had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.