Abstract
We investigate opinion dynamics and information spreading on networks under the influence of content filtering technologies. The filtering mechanism, present in many online social platforms, reduces individuals’ exposure to disagreeing opinions, producing algorithmic bias. We derive evolution equations for global opinion variables in the presence of algorithmic bias, network community structure, noise (independent behavior of individuals), and pairwise or group interactions. We consider the case where the social platform shows a predilection for one opinion over its opposite, unbalancing the dynamics in favor of that opinion. We show that if the imbalance is strong enough, it may determine the final global opinion and the dynamical behavior of the population. We find a complex phase diagram including phases of coexistence, consensus, and polarization of opinions as possible final states of the model, with phase transitions of different order between them. The fixed point structure of the equations determines the dynamics to a large extent. We focus on the time needed for convergence and conclude that this quantity varies within a wide range, showing occasionally signatures of critical slowing down and meta-stability.
Highlights
The collective behavior of a system made of interacting individuals can be successfully analyzed using agentbased models, as shown in many examples across various disciplines [1,2,3]
In [28], we have considered other archetypal models of opinion formation, and realized that the language model essentially interpolates between dynamics with either pairwise or group interactions depending on the value of α, and is a good candidate to explore the effects of bias asymmetry within a single model
In this paper we have studied the role of algorithmic bias and community structure in the potential rise of polarization of opinions in online social networks
Summary
The collective behavior of a system made of interacting individuals can be successfully analyzed using agentbased models, as shown in many examples across various disciplines [1,2,3]. The filtering algorithm requires the opinions of two individuals to be similar enough to be able to interact, and bias means that similar people with similar opinions have a greater chance to meet, leading to enhanced polarization and fragmentation in opinion space Another class of models considers opinions to be discrete (a binary variable in the simplest case) [21, 22]. An alternative implementation of algorithmic bias in binary-state models has been proposed in [24] In this case, the social platform records information about all the previous opinions of individuals, and influences them to keep the opinion that has been held for the longest time, to a memory or ‘inertia’ effect [25,26,27]. Throughout this paper we will pay special attention to the effect of algorithmic bias and its asymmetry
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