Positive algorithmic bias cannot stop fragmentation in homophilic networks

Bibliographic Data

ID10142595
AuthorsChris Blex (Oxford Internet Institute, University of Oxford, Oxford, UK), Taha Yasseri (0000-0002-1800-6094, Oxford Internet Institute, University of Oxford, Oxford, UK, corresponding author)
Year2020
Volume46
Issue1
Pages80-97
Publication date2020-09-13
Open AccessNo
TypeARTICLE
VenueJournal of Mathematical Sociology (JOURNAL)
Journal identifiersISSN: 0022-250X • E-ISSN: 1545-5874
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/0022250x.2020.1818078
OpenAlexW2999628059
LanguageEN
Citations received5
References cited49

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

Distributed computing · Fragmentation (computing · Homophily · Physics · Statistical physics · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Opinion Dynamics and Social Influence · Psychology · Social Psychology

Tools

Open DOIOpen Access