Network segregation and the propagation of misinformation

Abstract

How does the ideological segregation of online networks impact the spread of misinformation? Past studies have found that homophily generally increases diffusion, suggesting that partisan news, whether true or false, will spread farther in ideologically segregated networks. We argue that network segregation disproportionately aids messages that are otherwise too implausible to diffuse, thus favoring false over true news. To test this argument, we seeded true and false informational messages in experimental networks in which subjects were either ideologically integrated or segregated, yielding 512 controlled propagation histories in 16 independent information systems. Experimental results reveal that the fraction of false information circulating was systematically greater in ideologically segregated networks. Agent-based models show robustness of this finding across different network topologies and sizes. We conclude that partisan sorting undermines the veracity of information circulating on the Internet by increasing exposure to content that would otherwise not manage to diffuse.

Belief-consistent information is most shared despite being the least surprising

“…More generally, these results suggest that context-sensitive predictions are more accurate than those based on semantic-free information theoretic principles. Indeed, recent research documents that false partisan news propagates more quickly in ideologically segregated networks, seemingly because the implausibility of ideologically-inconsistent false news discourages diffusion in mixed networks 57 . This accords with other work on the propagation of misinformation in polarized “echo chambers” online 58 – 61 .…”

On the Intersection of Analytical Sociology and Computational Social Science

“…By contrast, most stances received bipartisan support and did not become aligned with any particular party in the independent conditions. Both experiments offer compelling demonstrations of the sensitivity of collective outcomes to cascades that are contingent on initial conditions and subsequent tipping points (see also Guilbeault, Baronchelli, & Centola, 2021;Stein, Keuschnigg, & van de Rijt, 2023).…”

“…Finally, we refer to two recent articles which integrate AS and CSS along the lines argued for here. Stein, Keuschnigg, and van de Rijt (2023) combine "multiple worlds" online experiments and agent-based modeling to assess the impact of ideological segregation on the propagation of misinformation in online networks. Arvidsson, Collet, and Hedström (2021) combine population registers, causal inference, machine learning, and empirically calibrated agent-based models to identify a novel network mechanism and test its macro-implications for the gender segregation of the Swedish labor market.…”

Attraction to Politically Extreme Users on Social Media

“…Political segregation represents a major challenge in the US and elsewhere. It is associated with attitudinal polarization, intergroup hostility, and increased spread of misinformation (Bishop, 2009;Iyengar et al, 2018;Stein et al, 2023) .…”