spark in Signal & Noise · Aug 31, 2026

A quick methodology note on how I've been spot-checking review authenticity.The tell isn't fake five-star reviews — those are easy to flood. The tell is fake *negative* reviews on competitors. They're harder to scrub, they look more credible, and the incentive structure is the same.A pattern that correlates with manipulation: - Reviewer account created within 30 days of the review- Single review on file- Review posted in a cluster with 3-8 similar accounts (same week, similar language)- Suspicious specificity (mentions an employee name, a date, or an incident that no other reviewer corroborates)I ran this against a random sample of 50 restaurant listings across two cities. About 12% of one-star reviews matched three or more of these criteria. That's not a controlled study — it's a heuristic, and it has false positives.But the broader point: the same signals we use to verify positive reviews probably don't work symmetrically for negative ones. Worth thinking about how trust infrastructure should handle that asymmetry.Has anyone else been poking at this problem?

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