ChamsPilot · Free Tools
Followers are one question. This one is different: are the likes and comments real? The free check reads the engagement on the recent posts against what creators this size on YouTube actually get.
YouTube's version differs from the others because the platform publishes less and its spam has its own economy.
Delivered in bulk after publishing, from accounts that never return. The mark is a like share far past the range for that channel size, with a comment count that stayed flat.
Usually promotional rather than complimentary: a crypto pitch, a Telegram handle, a fake reply impersonating the channel. High volume and unmistakable once read. It says more about moderation than about the creator, so it is weighed separately.
The case that inverts everything. Views bought without engagement push the like and comment shares far BELOW the range, so a channel can look under-engaged rather than over-engaged. The direction of the anomaly is what identifies it.
On YouTube the useful ratios are likes against views and comments against views, each compared with the range channels of that subscriber band measurably get rather than against a benchmark from an article. ChamsPilot builds those ranges per platform and per size, which is why a 200,000-subscriber channel is not judged by what a 2-million-subscriber channel does.
The like share is the first reading, and its direction matters as much as its size. Above the range means likes were added; far below it usually means views were, because bought views bring nobody who would press like.
Lifetime views against subscribers is the second, and it is specific to YouTube. A channel with a million subscribers and a lifetime view count that does not reflect them has either bought subscribers or lost the audience it had · and the recent uploads tell you which.
The third is consistency across uploads. Real channels have uneven videos. A channel where every upload collects nearly the same likes regardless of topic, length or how it performed is not describing viewers.
Both have 400,000 subscribers and both average 9,000 likes a video.
This is the case that inverts the usual intuition: Channel B's problem shows as engagement that is too LOW, not too high, because what was bought sits in the denominator. A check that only looks for suspiciously strong engagement walks straight past it.
| Channel A | Channel B | |
|---|---|---|
| Views a video | 180,000 | 620,000 |
| Likes a video | 9,000 | 9,000 |
| Likes as a share of views | 5.0%, inside the range | 1.5%, below the range for this size |
| Comments a video | 740 | 60 |
| Lifetime views per subscriber | 310 | 44 |
| Reading | A channel people watch | Views that arrived without viewers |
Identical subscriber counts and identical likes. Channel B looks bigger on the only number a media kit usually prints.
The free check reads the ratios. The paid audit reads the comment section, and separates two very different problems:
Crypto pitches and impersonation replies say something about moderation; bought comments say something about the creator. They are weighed separately instead of being added together.
Accounts with no uploads and no history, created in the same window, commenting on nothing else.
Whether comments trickle in over days, as they do on a real channel, or land within minutes of every publish.
What a placement is worth once the engagement that is not real is taken out of the calculation.
| Signal | What counts as normal | What gets flagged |
|---|---|---|
| Likes against views | Inside the range YouTube channels of that size measurably get | Far above it, or far below it |
| Comments against views | Comments in proportion to views, as elsewhere on the channel | Large view counts with almost no comments |
| Engagement across uploads | Varies with the video | Nearly identical on every upload |
| Lifetime views against subscribers | In the range for that subscriber count | Far below it, while recent uploads look strong |
| Comment text, authors and timing (AI Deep Check) | Comments about the video, from channels with history | Promotional text, new channels, bursts minutes after publishing |
YouTube publishes likes but not dislikes (removed publicly in 2021), and a channel can hide its like count entirely. A hidden counter is reported as not published, never as zero.
All Free Creator Tools Fake Follower Checker TikTok Fake Follower Checker Instagram Fake Follower Checker YouTube Fake Subscriber Checker YouTube Fake Views Checker TikTok Fake Views Checker Instagram Fake Views Checker TikTok Fake Engagement Checker Instagram Fake Engagement Checker Sponsorship Rate Calculator TikTok Money Calculator Instagram Earnings Calculator YouTube Money Calculator Influencer Earnings Estimator Public Snapchat Story Viewer AI Script Maker AI Brief Maker AI Contract Maker AI Contract Reviewer Free PDF Editor Free DOCX Editor Free Invoice Generator Brand Deal Scam Checker Brand Pitch Email Generator Brand Contact Email Finder Email Spam Checker Brand Deal Reply Generator AI Media Kit Generator TikTok Engagement Rate Calculator Instagram Engagement Rate Calculator YouTube Engagement Rate Calculator LinkedIn Engagement Rate Calculator Twitter / X Money Calculator Twitch Earnings Calculator Hashtag Generator UGC Contract Template Brand Outreach Email Template Free Media Kit Template Best Free Creator CRM
4.8 / 5 · 1,247 ratings
These ratings cover this tool and the others built on the same engine. They come from people we have worked with and from visitors rating here. How we collect them
Likes, comments, saves and shares that did not come from people who watched the post. They are sold by the thousand, traded in groups where members like each other's posts, or produced by comment farms that leave a few words under everything. It is a different problem from fake followers: an account can have a completely real audience and still buy likes on every post.
Compare the numbers with what creators of that size measurably get on YouTube, and with each other. Likes far past the range for the account's size, comments that do not grow with the likes, and engagement that is identical on every post regardless of how the post did are the three that show up without reading a single comment.
A group of creators who agree to like and comment on each other's posts, usually within minutes of publishing. From outside it shows as comments arriving in a tight burst right after posting, from the same accounts every time, with text that fits any post. The free check sees the shape; the AI Deep Check reads who the commenters are.
Yes. Every major platform's terms prohibit artificially inflating engagement, and all of them remove what they detect. The more practical risk for a brand is simpler: you are paying for an audience that will not buy anything, because it was never watching.
Compare likes with views, against the range channels of that size measurably get. Bought likes sit far above it while comments do not move. The reverse case matters too: bought VIEWS push the like share far below the range, because the views arrived without people.
Sometimes the topic simply does not invite comments, and sometimes comments are turned off · the check reports that rather than counting it as zero. But a channel whose videos consistently collect views with almost no conversation is describing traffic, not an audience.
Different from a pod. YouTube comment spam usually sells something: crypto, a Telegram handle, an impersonation of the channel owner replying to the top comment. It is high volume and easy to read, and it tells you about the channel's moderation rather than about bought engagement · which is why the audit separates the two.
Create a free ChamsPilot account
Read in another language: Français Español العربية 简体中文 繁體中文