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.

From mean-field to complex topologies : network effects on the algorithmic bias model

Pansanella, Valentina
;
2022

Abstract

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.
2022
Settore INF/01 - Informatica
COMPLEX NETWORKS 2021: The Tenth International Conference on Complex Networks and their Applications
Madrid
2021-30-11 – 2021-12-02
Complex Networks and Their Applications X : Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021
Springer International Publishing
978-3-030-93413-2
978-3-030-93412-5
Opinion dynamics; complex networks; algorithmic bias
   SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics
   SoBigData-PlusPlus
   European Commission
   Horizon 2020 Framework Programme
   871042
File in questo prodotto:
File Dimensione Formato  
COMPLEX_NETWORKS_2021_paper_38.pdf

Accesso chiuso

Tipologia: Published version
Licenza: Non pubblico
Dimensione 1.65 MB
Formato Adobe PDF
1.65 MB Adobe PDF   Richiedi una copia
From_mean_field_pre_print.pdf

accesso aperto

Tipologia: Submitted version (pre-print)
Licenza: Creative Commons
Dimensione 1.65 MB
Formato Adobe PDF
1.65 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/135525
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
  • OpenAlex ND
social impact