Plain-language answer
Is this repeated LinkedIn algorithm claim supported by dated evidence?
Reach Reality uses one versioned claims dataset to generate both the interactive checker and the human-readable Claims Index.
Short answer
Use evidence that can survive a second reader.
- Every verdict requires a stable ID, aliases, finding, primary source, action, category, evidence status, review dates, research version, and change note.
- The evidence-exposure score counts selected myths and contested claims; it does not predict reach.
- Material platform disclosures trigger review and the active dataset receives a full review at least quarterly.
Worked example
See the decision in a fictional case.
- Id
- reach-collapse-66pct
- Claim Text
- LinkedIn cut company-page organic reach 60-66% between 2024 and early 2026 to force companies into paid ads.
- Verdict
- MYTH
- Real Finding
- Metricool's 673,658-post, 63,108-account global study found company-page impressions per post down about 10% year over year (Jan-Feb 2025 vs Jan-Feb 2026). Larger account tiers grew; tiny and small tiers fell 27.52% and 12.19%. No transparent study supports a platform-wide 60-66% collapse.
- Do Instead
- Benchmark your own account's median reach across at least 10 posts before assuming a platform-wide collapse.
- Category
- Reach & Distribution
Use Reach RealityInspect the full methodTake it into the matching Petrichor work
What this answer cannot establish
- Observational platform studies do not establish causality unless the source does.