网络拓扑
计算机科学
同性恋
代表性启发
激进主义
理论计算机科学
数据科学
万维网
数学
政治学
计算机网络
社会科学
统计
社会学
法学
恐怖主义
作者
Valentina Pansanella,Giulio Rossetti,Letizia Milli
出处
期刊:Studies in computational intelligence
日期:2022-01-01
卷期号:: 329-340
被引量:2
标识
DOI:10.1007/978-3-030-93413-2_28
摘要
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on the formation and evolution of opinion in the context of a recent opinion dynamic model which includes bounded confidence and algorithmic bias. Mean-field, scale-free and random topologies, as well as networks generated by the Lancichinetti-Fortunato-Radicchi benchmark, are compared in terms of opinion fragmentation/polarization and time to convergence.
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