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 fragmentation and 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 opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.
Highlights
Political polarization and opinion fragmentation is a generally observed, ingravescent negative trend in modern western societies [1,2,3,4] with such concomitants as “alternative realities”, “filter bubbles”, “echo chambers”, and “fake news”
The aim of the present paper is to study this effect within a simple opinion dynamics model based on bounded confidence
In order to understand how the introduction of the algorithmic bias affects model performance, we study the model under multiple criteria for various combinations of parameters ε and γ
Summary
Political polarization and opinion fragmentation is a generally observed, ingravescent negative trend in modern western societies [1,2,3,4] with such concomitants as “alternative realities”, “filter bubbles”, “echo chambers”, and “fake news”. Several causes have been identified (see, e.g., [5]) but there is increasing evidence that new online media is one of them [6,7,8]. Earlier it was assumed that traditional mass media mostly influence the politically active elite of the society and they only indirectly affect polarization of the entire population.
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