Mass Manipulation in Simulated Social Networks: Dominating vs. Diversifying Attention

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 topics, which diversify attention. We also track dynamics across different network structures. We find that under acquaintance-based topics, a central MIA advances its propaganda effectively, causing volatile and polarized opinions through repeated exposure and echo chambers. Under randomized topics, this leverage disappears: the MIA's influence collapses across all network structures, and opinions become stable and broadly aligned with reality. These results, while deriving from simple simulations, align with realistic theories of bounded rationality and collective cognition, further suggesting a cognitively feasible, easy-to-monitor and robust strategy: distribute attention to combat misinformation.