If your team needs to move fast and avoid reputation hits, use our top 100 worst youtubers list to screen risky partners at a glance - filtered by audience quality, engagement anomalies, content relevance and geo-language fit. Create a free account to analyze any creator deeper.
We analyzed channels, audience signals and content to pinpoint the worst youtubers for quick screening - each entry scored on fraud risk, engagement anomalies, audience match and campaign reach.
Screen creators using audience quality, unusual engagement spikes, recent views and relevance to your brand. Use the list as a research starting point and inspect individual reports; a negative label alone is not evidence that a creator is unsuitable. A free account includes 10 email exports; larger exports need a paid plan or the Starter pass (50 email unlocks).
Seasonal slump — viewers slow down after holidays, creators post less, and churn spikes. So when you're asking why is january the worst month for youtubers, IQFluence shows rolling L30D views fall, growth % stalls and ER looks unreliable — exactly the signals that raise seasonal risk.
Profiles and stats refresh in seconds after you paste a creator link, with rolling last-30-day views and other key metrics updating immediately for fast verification.
Yes. After signing in you can export vetted creator outputs—Profile Analysis (PDF & JSON), saved lists (CSV), Media Plan Builder as a live Google Sheet, and Campaign Monitoring exports (CSV/Excel). Exports require an account and are available from the platform’s share/export controls.
We convert raw likes, comments and view counts into platform-neutral signals by using last-30-day averages and posting cadence context. ER% is calculated as L30D (likes+comments)/followers with cadence adjustments; views are reported as min/avg/max from recent-post distributions for fair cross-platform comparison.
We flag irregular patterns by comparing each creator’s last-30-day engagement to their typical cadence and to cohort benchmarks - spikes, repeated identical comment clusters, sudden subscriber jumps, or inflated min/avg/max view ratios trigger higher fraud-risk scores. Semantic checks and audience-quality signals reduce false positives.