Learning from Shared News: When Abundant Information Leads to Belief Polarization

Abstract

We study learning via shared news. Each period agents receive the same quantity and quality of first-hand information and can share it with friends. Some friends (possibly few) share selectively, generating heterogeneous news diets across agents akin to echo chambers. Agents are aware of selective sharing and update beliefs by Bayes' rule. Contrary to standard learning results, we show that beliefs can diverge in this environment leading to polarization. This requires that (i) agents hold misperceptions (even minor) about friends' sharing and (ii) information quality is sufficiently low. Polarization can worsen when agents' social connections expand. When the quantity of first-hand information becomes large, agents can hold opposite extreme beliefs resulting in severe polarization. We find that news aggregators can curb polarization caused by news sharing. Our results hold without media bias or fake news, so eliminating these is not sufficient to reduce polarization. When fake news is included, it can lead to polarization but only through misperceived selective sharing. We apply our theory to shed light on the evolution of public opinions about climate change in the US.

Conveying Message Distortion: A Synchronous and Asynchronous Approach to Effective Aviation Communication Services

“…From the problems mentioned above, each has dimensions according to the type of cause of message distortion. Several references state that the causes of message distortion in aviation communication services include physical disturbances (Santalova et al, 2019), language disorders (Dailidėnaitė & Volynec, 2017), cultural and language differences (Nijiati et al, 2020), no feedback (Nijiati et al, 2020), differences in status or position level (Jagiello & Hills, 2018), abundant information between communicators and communicants (Bowen et al, 2021).…”

“…, differences in status or level of office (Jagiello & Hills, 2018), abundant information between communicators and communicants (Bowen et al, 2021).…”

Link recommendation algorithms and dynamics of polarization in online social networks

“…Recent results indicate that opinion divergence can evolve concurrently with outgroup bias (12,13), which suggests combining opinions with dynamics on strategies, norms, reputations, and out-group/ingroup cooperation (74-77)-in line with some works in this Special Feature on polarization, cooperation, and group identity (78)(79)(80). While here we determine exogenously which nodes are polarizing or converging, future extensions can focus on explaining the mechanisms that determine why different individuals may react differently to outgroup opinions-an attitude that can depend on individuals' experience in contacting with diverse viewpoints (49) or on information quality, abundance, and individuals' assumptions about how informed their neighbors are (17). Also, we note that while here-and in related models (39,(53)(54)(55)(56)(57)-more extreme opinions are assumed to be more influential, alternative models assume that more extreme opinions can be less influential as moderate individuals may only listen to similar others and ignore distant opinions (35,37,38,40,81).…”

“…In future work we will study the impacts of link recommendation in bounded confidence models of opinion dynamics and in combination with homophilybased rewiring. Finally, future extensions shall address 1) the impact of structural similarity rewiring in combination with complex contagion (82,83), 2) the role of alternative link recommendation algorithms (25,26,29,43), or 3) individuals' heterogeneity regarding information sharing (17,83), information levels (58), influence (84) and susceptibility to the perils of online information consumption (85).…”

“…While traditional media outlets are curated by humans, online social media resorts to computer algorithms to personalize contents through automatic filtering. To understand information dynamics in online social networks, one needs to take into account the interrelated subtleties of human decision making [e.g., only share specific contents (17), actively engage with other users, follow or befriend particular individuals, interact offline] and the outcomes of automated decisions (e.g., news sorting and recommendation systems) (18,19). In this regard, much attention has been placed on the role of news filters and sorting (1,18,19).…”