We prove which Scene YouTubers drive rapid reach and real engagement - struggling to spot authentic creators among noise? Explore this verified Top-11, review AI-backed metrics, then start a 7-day trial to export contacts and launch fast.
Browse this Top-11 snapshot of scene youtubers - handpicked via semantic search, fraud checks, engagement metrics and audience-ready export for quick launch
How quickly do profiles and metrics refresh after I paste a profile?
Profiles and stats refresh in seconds after you paste a handle. IQFluence pulls rolling last-30-day metrics immediately so you see up-to-the-moment ER%, views, cadence and audience snapshots for quick verification and shortlisting.
Can I compare these YouTubers side-by-side across platforms?
Yes. The platform standardizes Instagram, TikTok and YouTube metrics - ER%, views and posting cadence - so you can place creators side-by-side in one grid, compare audience demographics and performance diagnostics, and export apples-to-apples rows for shortlisting.
How does IQFluence detect audience fraud and fake followers?
We analyze accounts and their followers versus behavior baselines - engagement patterns, sudden growth, view-to-follower ratios, and audience overlap. Semantic signals and reachability flags expose bot-like activity. Results feed a fraud score so you can spot suspicious audiences before shortlisting.
Can I export these Top-11 creator contacts for outreach?
Yes — exports require signing in. Once you start the 7-day trial you can save favorites and export creator contacts (CSV/Google Sheet/PDF/JSON) from the Media Plan Builder and Profile Analysis. Outreach fields (email/WhatsApp/phone/Skype/Kakao/WeChat/Viber) are included where available.
How are min/avg/max views calculated for creator projections?
We derive min/avg/max views from each creator’s recent-post distribution over the last 30 days - the system calculates a conservative minimum, a central average, and an optimistic maximum from those recent-view samples, normalized by cadence so the bands reflect realistic low→mid→high scenarios you can use in your own forecasts.
How do you normalize ER% and views for fair cross-platform comparison?
We adjust for platform quirks by using last-30-day averages and posting cadence: ER% is likes+comments relative to followers averaged over recent posts, and views use per-post distributions. Both metrics are normalized across platforms so cadence, typical view rates, and sample windows align for fair apples-to-apples comparisons.