Positive algorithmic bias cannot stop fragmentation in homophilic networks

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Title: Positive algorithmic bias cannot stop fragmentation in homophilic networks
Authors: Blex, ChrisYasseri, Taha
Permanent link: http://hdl.handle.net/10197/12719
Date: 13-Sep-2020
Online since: 2022-01-12T12:49:52Z
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.
Funding Details: Engineering and Physical Sciences Research Council
Type of material: Journal Article
Publisher: Taylor & Francis
Journal: Journal of Mathematical Sociology
Copyright (published version): 2020 the Authors
Keywords: Algorithmic biasEcho chambersHomophilySchelling modelSocial networksSchelling segregrationModelEmergenceFeatherOnlineMediaBirdsNews
DOI: 10.1080/0022250X.2020.1818078
Language: en
Status of Item: Peer reviewed
ISSN: 0022-250X
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Sociology Research Collection
Geary Institute Research Collection

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