Simulating Strategies for Defense Against Brand-Targeted Online Disinformation

arXiv:2610.07407v1 Announce Type: cross Abstract: Brand-targeted disinformation can damage firms not only by spreading false beliefs, but by eroding trust, amplifying reputational uncertainty, and persisting across networked digital environments. This paper models brand-targeted disinformation as a trust-weighted contagion process using a multi-agent simulation across six network structures, three amplification levels, and six intervention strategies. Results show that scale-free and influencer-heavy networks are most vulnerable to rapid spread, while clustered and polarized networks provide partial containment until amplification bridges communities. Hybrid intervention combining prebunking, trusted-node activation, and rapid correction performs best overall. The paper contributes a reusable simulation framework for studying active brand disinformation defense strategies.

Disinformation can erode trust and damage firms.

The study is a theoretical/simulation contribution to disinformation defense, not a direct statement about AI's societal impact; it has modest practical value but no strong evidence of real-world effect either way.

The paper acknowledges that brand-targeted disinformation can damage firms by spreading false beliefs, eroding trust, and amplifying reputational uncertainty. It aims to develop defense strategies, implying a negative impact from AI-driven disinformation on commercial stability and consumer trust, despite the focus on mitigation.

The paper presents a model and simulation framework for defending against brand-targeted disinformation. It identifies effective intervention strategies, which could significantly mitigate the negative impacts of disinformation on firms and trust. The focus is on practical solutions to a pressing societal issue.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   220506e4-5d86-4a73-ac4c-7778034ee180
           https://arxiv.org/abs/2610.07407
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EVIDENCE   2 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -22  (Adverse)
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CONFIDENCE 75%