Algorithmic bias amplifies opinion polarization: A bounded confidence model

TL;DR

This paper modifies a bounded confidence opinion model to include algorithmic bias, showing that such bias increases societal polarization and slows convergence, especially with fragmented initial opinions.

Contribution

It introduces a new selection rule in the opinion dynamics model to simulate online media bias, revealing its effects on polarization and convergence speed.

Findings

Algorithmic bias increases opinion polarization.

Bias causes a slowdown in convergence to consensus.

Fragmented populations amplify polarization effects.

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.