Political polarization and opinion fragmentation is a generally observed, ingravescent negative trend in modern western societies with such concomitants as “alternative realities”, “filter bubbles”, “echo chambers”, and “fake news”. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All results from simulations are included in the manuscript.įunding: This work was supported by the European Community’s H2020 Program under the funding scheme “FETPROACT-1-2014: Global Systems Science (GSS)", grant agreement # 641191 to AS, DP, FG and JK, CIMPLEX “Bringing CItizens, Models and Data together in Participatory, Interactive SociaL Exploratories.” The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. Received: MaAccepted: FebruPublished: March 5, 2019Ĭopyright: © 2019 Sîrbu et al. PLoS ONE 14(3):Ĭentre National de la Recherche Scientifique, FRANCE Fragmentation and polarization are augmented by a fragmented initial population.Ĭitation: Sîrbu A, Pedreschi D, Giannotti F, Kertész J (2019) Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model. 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. 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. 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. 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. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate.
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