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Article overview
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Modeling communication asymmetry and algorithmic personalization in online social networks | Franco Galante
; Luca Vassio
; Michele Garetto
; Emilio Leonardi
; | Date: |
4 Jan 2023 | Abstract: | Modeling social interactions and their impact on opinion dynamics has
attracted growing interest in recent decades, fuelled by the mounting
popularity of online social networks (OSNs). On online social platforms, a few
individuals, commonly referred to as extit{influencers}, produce the majority
of content consumed by users. However, classic opinion models do not capture
this communication asymmetry in OSNs. We develop an opinion model inspired by
observations on leading social media platforms and tailored to the
peculiarities of online interactions. Our work has two main objectives: first,
to describe the inherent communication asymmetry in OSNs, where a tiny group of
extit{influencers} hegemonizes the landscape of social debate, and second, to
model the personalization of content by the social media platform. We derive a
Fokker-Planck equation for the temporal evolution of users’ opinion
distribution and analytically characterize the stationary system behavior.
Analytical results, confirmed by Monte Carlo simulations, show how content
personalization tends to radicalize user opinion and favor structurally
advantaged influencers. These emerging behaviors suggest that algorithmic bias,
inherently associated with platform filtering, can lead to undesirable
outcomes. As an example application, we apply our model to Facebook during the
Italian government crisis in the summer of 2019. Our work provides a flexible
framework to assess the impact of algorithmic filtering on the opinion
formation process and a fine-grained tool to study the complex interaction
between influencers and social network users. | Source: | arXiv, 2301.01478 | Services: | Forum | Review | PDF | Favorites |
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