Positive algorithmic bias cannot stop fragmentation in homophilic networks

被引:16
作者
Blex, Chris [1 ,2 ]
Yasseri, Taha [1 ,2 ,3 ]
机构
[1] Univ Oxford, Oxford Internet Inst, Oxford, England
[2] Alan Turing Inst, London, England
[3] Univ Coll Dublin, Sch Sociol, Dublin, Ireland
基金
英国工程与自然科学研究理事会;
关键词
Algorithmic bias; echo chambers; homophily; Schelling model; social networks; SCHELLING SEGREGATION; MODEL; EMERGENCE; FEATHER; ONLINE; MEDIA; BIRDS;
D O I
10.1080/0022250X.2020.1818078
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页码:80 / 97
页数:18
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