How the analysis works
Published in full, because a vetting tool that will not show its working is asking for the same blind trust it is meant to replace.
Where does Ostryno get its data?
Everything is retrieved from official platform APIs. Instagram data comes through the Instagram Graph API, which returns public profile and post metrics for Business and Creator accounts. YouTube data comes through the YouTube Data API v3, which returns public channel and video statistics.
We do not scrape, we do not buy panel data, and we do not ask the creator to connect anything. If an Instagram account is set to Personal, no API exposes it and we cannot analyse it. No credit is charged in that case.
What signals does Ostryno measure?
No platform publishes a list of an account's followers, so nobody can directly count real versus fake. What is observable is behaviour. Real audiences watch, like and comment. Purchased ones do not. Each signal below is measured, scored, and weighted.
Engagement vs tier
35%Engagement rate measured against the benchmark for accounts of that size.
A 2% engagement rate is strong for a 500K account and weak for a 5K one. Comparing against a flat number punishes large accounts and flatters small ones, so we compare each account to its own tier.
Comment quality
20%Comments as a proportion of likes.
This is the cleanest fake-engagement signal available. Likes can be bought for pennies. Comments that read as human are far harder and more expensive to fake, so bought engagement shows up as a lopsided ratio. Below roughly 0.5% is a floor; a healthy band runs from about 1.5% to 25%.
Reach efficiency
25%Typical views or impressions relative to follower count, smoothed against a platform baseline.
Bought followers do not watch. An account whose reach has collapsed relative to its size is showing the clearest symptom of an inflated follower count.
Posting consistency
10%Average posting frequency across the recent sample.
A large account that has not posted in months is either dormant or never built a real audience habit. Weighted lightly, because plenty of good creators post infrequently on purpose.
Audience integrity
10%Followers relative to accounts followed.
Follow-for-follow and mass-follow growth leave an obvious fingerprint. Capped, so a very high ratio confers no extra advantage. Instagram only: YouTube publishes no subscription count, so this signal is dropped there and its weight is redistributed across the other four.
Signals for YouTube are weighted differently, because the view-to-subscriber relationship carries far more information there than it does on Instagram.
What is a good engagement rate on Instagram?
It depends entirely on account size, because engagement falls as an audience grows. Judging a million-follower account against a small account's benchmark is the most common mistake in creator vetting. These are the exact thresholds Ostryno measures Instagram accounts against.
| Tier | Followers | Benchmark |
|---|---|---|
| Nano | Under 10,000 | 6.0% |
| Micro | 10,000 to 50,000 | 4.5% |
| Macro | 50,000 to 100,000 | 3.0% |
| Mega | 100,000 to 500,000 | 2.0% |
| Elite | 500,000 to 1 million | 1.5% |
| Celebrity | Over 1 million | 1.0% |
Hitting the benchmark scores roughly 70. Double it scores 100, half it scores about 35. An account running below a third of its tier benchmark triggers the engagement collapse flag below.
What gets flagged as suspicious?
Separately from the weighted signals, a handful of specific patterns are checked and reported as triggered or not. These are the combinations that rarely occur naturally.
How is the written analysis produced?
The computed signals are passed to a large language model, which writes the plain-English read: what the combination indicates, which reading is more likely when two signals conflict, and what is worth checking by hand. Only the computed metrics are sent. Your account details are not.
What can't this analysis tell you?
- It cannot prove that a specific follower is fake, or that a creator bought anything. It reports patterns, and patterns have innocent explanations.
- It does not produce a score, a rating or a grade, and it does not rank creators against each other on a single number.
- It cannot see private data: follower demographics, story metrics, or anything the platform does not expose publicly.
- It cannot tell you whether an audience will buy. Authenticity and commercial fit are different questions.
Ostryno gives you the lens. The call is still yours.