Algorithmic bias amplifies opinion polarization: A bounded confidence model

Abstract

The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards polarization, which emerges also in conditions where the original model would predict convergence, and b) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Polarization is augmented by a fragmented initial population.

References in corpus (19)

• Emergence of scaling in random networks
• Epidemic processes in complex networks
• Continuous Opinion Dynamics under Bounded Confidence: A Survey
• Consensus formation on adaptive networks
• Emergence of communities in weighted networks
• Individualization as driving force of clustering phenomena in humans
• Opinion dynamics: models, extensions and external effects
• Bounded confidence and Social networks
• Structural transition in social networks: The role of homophily
• Geographies of an online social network
• Universality of the Threshold for Complete Consensus for the Opinion Dynamics of Deffuant et al
• Simulation of Consensus Model of Deffuant et al on a Barabasi-Albert Network
• NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks
• Multilayer weighted social network model
• The role of noise and initial conditions in the asymptotic solution of a bounded confidence, continuous-opinion model
• The role of homophily in the emergence of opinion controversies
• Opinion dynamics with disagreement and modulated information
• Anomalous Critical Slowdown at a First Order Phase Transition in Single Polymer Chains
• Polarization Rank: A Study on European News Consumption on Facebook

Cited by in corpus (13)

• Recent advances in opinion propagation dynamics: A 2020 Survey
• From classical to modern opinion dynamics
• Emergence of polarization in a voter model with personalized information
• The effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinions
• Positive algorithmic bias cannot stop fragmentation in homophilic networks
• The effect of Collaborative-Filtering based Recommendation Algorithms on Opinion Polarization
• Opinion formation on social networks with algorithmic bias: Dynamics and bias imbalance
• How Social Rewiring Preferences Bridge Polarized Communities
• Filter Bubble effect in the multistate voter model
• From mean-field to complex topologies: network effects on the algorithmic bias model
• Collective patterns and stable misunderstandings in networks striving for consensus without a common value system
• Voter-like dynamics with conflicting preferences on modular networks
• Polarization and Belief Convergence of Agents in Strongly-Connected Influence Graphs