The triple-filter bubble: Using agent-based modelling to test a meta-theoretical framework for the emergence of filter bubbles and echo chambers

Authored by Daniel Geschke, Jan Lorenz, Peter Holtz

Date Published: 2019

DOI: 10.1111/bjso.12286

Sponsors: German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)

Platforms: NetLogo

Model Documentation: Other Narrative

Model Code URLs: https://zenodo.org/record/1407733#.Xiy0pFNKi3I

Abstract

Filter bubbles and echo chambers have both been linked recently by commentators to rapid societal changes such as Brexit and the polarization of the US American society in the course of Donald Trump's election campaign. We hypothesize that information filtering processes take place on the individual, the social, and the technological levels (triple-filter-bubble framework). We constructed an agent-based modelling (ABM) and analysed twelve different information filtering scenarios to answer the question under which circumstances social media and recommender algorithms contribute to fragmentation of modern society into distinct echo chambers. Simulations show that, even without any social or technological filters, echo chambers emerge as a consequence of cognitive mechanisms, such as confirmation bias, under conditions of central information propagation through channels reaching a large part of the population. When social and technological filtering mechanisms are added to the model, polarization of society into even more distinct and less interconnected echo chambers is observed. Merits and limits of the theoretical framework, and more generally of studying complex social phenomena using ABM, are discussed. Directions for future research such as ways of comparing our simulations with actual empirical data and possible measures against societal fragmentation on the three different levels are suggested.

Tags

Agent-based modelling Social media Recommender systems Self Bias Selective exposure Attitude polarization News Echo chamber effect Filter bubble