Polarized information ecosystems can reorganize social networks via information cascades

Abstract

The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.

Echo chambers and information transmission biases in homophilic and heterophilic networks

“…Another variation of these models could also incorporate a coevolving network in which nodes rewire to maximize the number of neighbors with their same opinion. This rewiring is known to lead to network polarization, echo chambers and ultimately fragmentation 8,45,46 , but its influence on the other biases is unknown. Finally, the generalization of the model to multidimensional topic spaces could help in the understanding of how ideologies form 6 .…”

“…Information transmission in the context of Information and Communication Technologies is a great opportunity to create a better-informed society, but in practice these technologies are also promoting phenomena such as viral spreading of fake news [1][2][3] , echo chambers [4][5][6] , perception biases such as false consensus or majority illusions 7 , as well as social polarization 5,6,8 . We understand by echo chambers situations in which the transmission of information among individuals belonging to the same opinion group is dominant, while transmission among individuals with different opinions is hindered.…”

Multipolar social systems: Measuring polarization beyond dichotomous contexts

“…Polarization is also closely linked to the way individuals from different social groups interact, so to get a complete understanding of polarization, it is necessary to consider not only the ideological stances of the polarized set of individuals or parties, but also the relationships among them [38,39,40]. After all, the formation of social ties between individuals is often related to their ideological affinity [41], potentially leading to network polarization [42,43]; that is, the organization of a social network in highly connected subgroups (or clusters) with weak inter-group connectivity [44,21,45]. Taking this into account, we use an opinion inference framework based on networks of user interactions in online social media [46] (although the framework can be applied to any networked system).…”

The dynamics of political polarization

“…Tokita et al (15) further explore social networks, and specifically the role of the information network in driving polarization. They present a model in which individuals gain information through their social networks, and potentially change their networks based on the information cascades to which they are exposed.…”

“…Risk aversion and inequality can produce a runaway process of polarization over time. Using computational models on social media data, Tokita et al (15) model how information cascades can reorder social ties and lead to increasingly polarized networks. The evolution of the system in these models is of most importance.…”