Effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinions

| openaire: EC/H2020/952026/EU//HumanE-AI-Net | openaire: EC/H2020/871042/EU//SoBigData-PlusPlus | openaire: EC/H2020/810115/EU//DYNASNET Funding Information: We acknowledge support from AFOSR (Grant No. FA8655-20-1-7020), project EU H2020 Humane AI-net (Grant No. 952026), and CHIST-ERA project SAI (Grant No. FWF I 5205-N). J.K. is grateful for support by projects ERC DYNASNET Synergy (Grant No. 810115) and EU H2020 SoBigData (Grant No. 871042). All authors conceived and designed the research. A.F.P. derived all analytical calculations, performed numerical simulations, and analyzed the data. M.N. and G.I. derived effective rates in presence of bias. A.F.P., J.K., and G.I. wrote the manuscript. All authors approved the manuscript. Publisher Copyright: ©2021 American Physical SocietyIndividuals of modern societies share ideas and participate in collective processes within a pervasive, variable, and mostly hidden ecosystem of content filtering technologies that determine what information we see online. Despite the impact of these algorithms on daily life and society, little is known about their effect on information transfer and opinion formation. It is thus unclear to what extent algorithmic bias has a harmful influence on collective decision-making, such as a tendency to polarize debate. Here we introduce a general theoretical framework to systematically link models of opinion dynamics, social network structure, and content filtering. We showcase the flexibility of our framework by exploring a family of binary-state opinion dynamics models where information exchange lies in a spectrum from pairwise to group interactions. All models show an opinion polarization regime driven by algorithmic bias and modular network structure. The role of content filtering is, however, surprisingly nuanced; for pairwise interactions it leads to polarization, while for group interactions it promotes coexistence of opinions. This allows us to pinpoint which social interactions are robust against algorithmic bias, and which ones are susceptible to bias-enhanced opinion polarization. Our framework gives theoretical ground for the development of heuristics to tackle harmful effects of online bias, such as information bottlenecks, echo chambers, and opinion radicalization.Peer reviewe