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Modelling opinion dynamics in the age of algorithmic personalisation

Modelling opinion dynamics in the age of algorithmic personalisation

Correa Da Rocha, Luis ORCID: 0000-0001-9046-8739 and Perra, Nicola ORCID: 0000-0002-5559-3064 (2019) Modelling opinion dynamics in the age of algorithmic personalisation. Scientific Reports, 9:7261. ISSN 2045-2322 (Online) (doi:

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Modern technology has drastically changed the way we interact and consume information. For example, online social platforms allow for seamless communication exchanges at an unprecedented scale. However, we are still bounded by cognitive and temporal constraints. Our attention is limited and extremely valuable. Algorithmic personalisation has become a standard approach to tackle the information overload problem. As result, the exposure to our friends' opinions and our perception about important issues might be distorted. However, the effects of algorithmic gatekeeping on our hyper-connected society are poorly understood. Here, we devise an opinion dynamics model where individuals are connected through a social network and adopt opinions as function of the view points they are exposed to. We apply various filtering algorithms that select the opinions shown to each user i) at random ii) considering time ordering or iii) its current opinion. Furthermore, we investigate the interplay between such mechanisms and crucial features of real networks. We found that algorithmic filtering might influence opinions' share and distributions, especially in case information is biased towards the current opinion of each user. These effects are reinforced in networks featuring topological and spatial correlations where echo chambers and polarisation emerge. Conversely, heterogeneity in connectivity patterns reduces such tendency. We consider also a scenario where one opinion, through nudging, is centrally pushed to all users. Interestingly, even minimal nudging is able to change the status quo moving it towards the desired view point. Our findings suggest that simple filtering algorithms might be powerful tools to regulate opinion dynamics taking place on social networks.

Item Type: Article
Uncontrolled Keywords: social networks, opinion dynamics, social influence, bias
Subjects: H Social Sciences > H Social Sciences (General)
Faculty / School / Research Centre / Research Group: Faculty of Business
Faculty of Business > Department of International Business & Economics
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified: 21 Oct 2020 10:05

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