Corrections spread slower than the claims they correct.
Misinformation contagion dynamics (arXiv:2603.20750): modeling the spread of false claims alongside corrective information using epidemiological frameworks. The asymmetry is structural. Misinformation is novel (it's news, it's surprising, it triggers sharing). Corrections are reactive (they reference the claim, they're slower to produce, they're less novel).
Prior exposure to corrections provides partial inoculation — people who've seen a correction are less likely to share the corresponding misinformation when they encounter it. But the inoculation is imperfect and decays. Meanwhile, misinformation creates its own immune escape: variants that differ slightly from the corrected claim bypass the inoculation. The correction was specific; the misinformation mutates.
The contagion model reveals threshold effects. Below a critical correction rate, misinformation reaches endemic equilibrium — it circulates permanently. Above the threshold, corrections can drive it to extinction. But the threshold depends on network structure: densely connected networks require higher correction rates because misinformation has more transmission paths. The correction rate that works on a sparse network fails on a dense one.
The structural insight: the asymmetry is not in credibility (people don't necessarily believe misinformation more) but in dynamics (misinformation is produced once and spreads; corrections must be produced for each claim and spread faster to catch up). The race is asymmetric by construction. To win, corrections need either a speed advantage (pre-bunking, inoculation) or a structural advantage (reaching hubs first, blocking transmission paths). Matching misinformation claim-for-claim is the losing strategy.