Mass Manipulation in Simulated Social Networks: Dominating vs. Diversifying Attention
Viktoria Kainz, Justin Sulik, Anna Neudert, and Torsten En{\ss}lin

TL;DR
This study shows that diversifying attention in social networks can significantly reduce the influence of misinformation campaigns, leading to more stable and reality-aligned opinions, unlike traditional familiarity-based attention.
Contribution
It demonstrates through agent-based modeling that topic diversification effectively mitigates misinformation influence, offering a simple, cognitively feasible strategy for societal resilience.
Findings
Diversified attention collapses MIA influence across network types.
Acquaintance-based attention leads to echo chambers and polarization.
Randomized attention results in stable, reality-aligned opinions.
Abstract
Modern information environments, especially social media, are highly complex systems that exceed individual processing capacities such as humans' limited attention. This environment/cognition mismatch can increase susceptibility to misinformation, which various actors exploit for anti-social (including anti-democratic or anti-science) aims. This raises the question of how to feasibly sustain societal resilience against misinformation, though the challenge is to find strategies that respect individuals' cognitive limitations. We investigate whether a simple behavioral rule - topic diversification - can enhance collective performance and mitigate vulnerability. In an agent-based model that includes a deceptive mass-influencing agent (MIA), we compare two attention-distribution strategies: (A) acquaintance-based topic selection, where agents return to familiar content, and (B) randomized…
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Taxonomy
TopicsMisinformation and Its Impacts · Opinion Dynamics and Social Influence · Social Media and Politics
