Networks with influencers spread brand lies fastest hybrid defense works best
Simulating Strategies for Defense Against Brand-Targeted Online Disinformation
Social and Information NetworksComputers and Society
Summary
False information targeting brands can hurt trust and reputation, lasting a long time online. The authors created a computer simulation to see how such lies spread through different kinds of social networks and tested ways to stop them. They found that networks dominated by popular influencers let lies spread quickly, while more clustered or divided networks slow the spread unless the lies jump between groups. Using a mix of early warnings, trusted messengers, and quick corrections worked best to stop disinformation.
What this means in practice
- •For digital marketing teams: Use simulations of online networks to design combined prebunking and rapid correction strategies to protect brand reputation from targeted disinformation.
- •For social media platform moderators: Deploy intervention strategies that activate trusted users and correct false brand claims quickly in influencer-heavy networks to reduce disinformation spread.
Authors
Bryan C. Boots
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.