How to Get YouTube Channel and Comment Data for Creator Research

Say you run partnerships for a B2B SaaS company, and you're deciding between three mid-size tech-review channels for a sponsorship. All three list similar subscriber counts on their About pages. Subscriber count alone won't tell you which one is actually worth the spend — a channel that hasn't posted in three months and one uploading weekly can show the same number.

You need three things layered together: who the channel actually is, whether their recent uploads are gaining or losing traction, and whether the audience in the comments is engaged or checked out. A subscriber count is a number a channel earned over years, possibly including years it's no longer active. It tells you almost nothing about this month.

If you're an agent (or building one) reading this rather than a human, the full machine-readable schema for every endpoint below lives at mindcase.co/skills.md.

What makes YouTube channel data useful for creator research?

A handful of signals separate a channel worth partnering with from one that looks good on paper:

• Upload recency. A channel that stopped posting months ago isn't reaching anyone right now, no matter what its subscriber count says.
• Views relative to subscriber count. A channel averaging far fewer views per video than its subscriber count would suggest is losing reach, even if the total audience number still looks healthy.
• Engagement ratio. Comments and likes relative to views tell you whether people are actually reacting, not just scrolling past.
• Comment sentiment. Raw comment volume doesn't distinguish real enthusiasm from backlash — you have to read a sample to tell the difference.

Subscriber count is the number every channel leads with. It's also the easiest number to have accumulated years ago and never earned since. Join date is worth checking too, for the opposite reason: a channel with a large audience and a join date from three months ago got there fast, and fast growth is its own signal worth understanding before you factor it into a decision either way.

How do you find and evaluate a channel?

Start from a handle you already have, or a keyword if you're scouting.

curl -X POST "https://api.mindcase.co/v1/data/youtube/channels/run?wait=true" \
-H "Authorization: Bearer $MINDCASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "params": {
    "query": "tech review"
  }
}'

# $0.0005 per channel

Each row returned by YouTube Channels API is a full profile — 21 fields, including subscriber count, total views, total video count, join date, and verification status. Divide total views by total videos and you already have a rough average-views-per-video figure without touching a single upload yet.

How do you pull a channel's videos and measure real momentum?

Subscriber count is a lifetime total. Recent uploads are where you find out if a channel is still growing.

curl -X POST "https://api.mindcase.co/v1/data/youtube/videos/run?wait=true" \
-H "Authorization: Bearer $MINDCASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "params": {
    "channelUrl": "https://www.youtube.com/@example-tech-channel",
    "onlyVideosNewerThan": "2026-09-01",
    "maxResults": 20
  }
}'

# $0.0005 per video

YouTube Videos API returns 21 fields per video — views, likes, comment count, duration, and publish date among them. onlyVideosNewerThan is the useful part for tracking, not just a one-off pull: filtering happens before any video is fetched, so a channel that hasn't posted since your last check returns nothing and costs nothing. Pull once broadly to set a baseline, then re-run with a rolling date filter — weekly, say — and you're tracking upload frequency and view velocity over time instead of guessing from a single snapshot.

Compare across the channels you're evaluating, not just within one. A video with 50,000 views and 4,000 combined likes and comments is showing more real interest than one with 200,000 views and 800 — raw view count alone doesn't separate an audience that cares from one that scrolled past.

Back to the three sponsorship candidates: say all three list roughly 80,000 subscribers on their About pages. Pulling their last eight uploads tells a different story. Channel A is averaging 60,000 views a video with a combined like-and-comment count around 3,000 — a 5% engagement ratio. Channel B is averaging 12,000 views on the same eight uploads, with barely 200 combined reactions — under 2%, and well below what its subscriber count would predict. Channel C hasn't posted in five weeks. The subscriber numbers alone made all three look interchangeable. The video data makes the decision for you.

How do you read the comments to tell real enthusiasm from noise?

View and like counts don't tell you what people are actually saying.

curl -X POST "https://api.mindcase.co/v1/data/youtube/comments/run?wait=true" \
-H "Authorization: Bearer $MINDCASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "params": {
    "videoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "maxResults": 200
  }
}'

# $0.0005 per comment

YouTube Comments API returns 14 fields per comment — the text, like count, reply count, and whether the channel owner replied or hearted it. Run the top few hundred comments from a recent upload through an LLM to tag sentiment before you read a single one yourself: a video with heavy comment volume that's mostly complaints about a sponsor read is a different signal than the same volume genuinely discussing the content.

Two fields worth pulling out specifically: authorIsOwner flags whether the creator themselves replied, and each comment's own likes count tells you which reactions the audience surfaced as worth reading. A channel where the creator replies to real questions in the comments is showing a different relationship with its audience than one where every well-liked comment goes unanswered. Neither shows up in the video metadata alone — you only see it by pulling the comments.

What can't you get from Mindcase?

Two cases.

The video's transcript or spoken content. These endpoints return metadata — title, description, view and engagement counts, timestamps — not a transcript. If your research depends on what was actually said in the video rather than how it performed, that's outside what this API returns.

Historical stats from before your first pull. Every field here reflects the channel or video's state right now. onlyVideosNewerThan filters which uploads come back, not what their view counts looked like last month — there's no built-in time-series, only what you build by pulling on a schedule and keeping your own history.

What's the bottom line?

A subscriber count answers one question: how large is this channel's audience, historically. It doesn't answer whether that audience is still there this month, or whether they react to what gets posted. Pull recent uploads to check momentum, pull comments to check whether the reaction is real, and let the two together — not the headline number — decide who gets the sponsorship, the interview request, or the competitive research slot.

Which endpoint should you use for which job?

FAQ