DEV Community: KinderBB

Find YouTube Channels by Topic, Language, and Country

KinderBB — Tue, 25 Aug 2026 00:30:02 +0000

Most tools that find YouTube channels by topic treat the search as one keyword and one result page. That works for lookup. It misses how creator discovery behaves across languages, countries, and niche vocabulary.

“AI automation” in English, “automatisation IA” in French, and “automatización con IA” in Spanish do not return the same creator pool. Country filters add another problem: a creator’s language, search region, declared country, and inferred geography are not interchangeable.

Here is a reproducible way to discover channels without building a seed list first.

Start from a market matrix

Write the requested market as separate dimensions:

niches = ["AI automation", "B2B SaaS"]
languages = ["en", "fr"]
countries = ["US", "CA", "GB", "FR"]

This is more useful than concatenating everything into one long search phrase. It lets the discovery layer try multiple relevant combinations while keeping a global cap on search calls.

The channel URLs you already know can still be included as optional seeds. They should not define the whole market.

Use worldwide language mode when country is not the brief

A French-language campaign is not necessarily a France campaign.

French-speaking creators can publish from Canada, Belgium, Switzerland, Morocco, Senegal, or anywhere else. If language matters and location does not, use:

run_input = {
    "preset": "qualified_shortlist",
    "niches": ["AI agents", "business automation"],
    "languages": ["fr"],
    "countries": [],
    "worldwideLanguageMode": True,
    "maxChannels": 10,
    "videosAnalyzedPerChannel": 10,
    "maximumSearchCalls": 12,
}

This guides discovery by language without pretending that every returned creator lives in one country.

Run the discovery from Python

pip install apify-client
export APIFY_API_TOKEN="..."

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_API_TOKEN"])

run_input = {
    "preset": "qualified_shortlist",
    "searchQueries": [
        "AI automation tutorials",
        "B2B SaaS growth",
    ],
    "niches": ["AI automation", "B2B SaaS"],
    "languages": ["en", "fr"],
    "countries": ["US", "CA", "GB", "FR"],
    "worldwideLanguageMode": False,
    "excludedKeywords": ["compilation", "reupload"],
    "minSubscribers": 1_000,
    "maxSubscribers": 500_000,
    "minVideosPerMonth": 1,
    "lastUploadWithinDays": 90,
    "maxChannels": 10,
    "videosAnalyzedPerChannel": 10,
    "maximumSearchCalls": 16,
}

run = client.actor("kazkn/youtube-creator-lead-finder").call(
    run_input=run_input,
)

items = client.dataset(run["defaultDatasetId"]).list_items().items

The output is deduplicated by stable channelId inside the run. That matters because the same creator can match several topics, languages, country hints, or search phrases.

Preserve what matched

A useful discovery row should explain why the channel entered the shortlist.

for item in items:
    print({
        "channel": item["channelName"],
        "matched_queries": item.get("matchedQueries", []),
        "matched_languages": item.get("matchedLanguages", []),
        "matched_countries": item.get("matchedCountries", []),
        "primary_language": item.get("primaryLanguage"),
        "declared_country": item.get("declaredCountry"),
        "inferred_countries": item.get("inferredCountries", []),
        "warnings": item.get("warnings", []),
    })

The matched* fields describe the search request. They are not automatically confirmed creator attributes.

Declared country is not inferred country

When public channel metadata includes a country, keep it as declaredCountry.

When geography is inferred from public signals, keep it in inferredCountries with countryConfidence. Never overwrite the declared field with a guess. If no defensible location signal exists, leave it unknown.

The same rule applies to language. Keep the primary language, detected languages, and confidence visible instead of turning a search hint into a fact.

Qualify after discovery

Topic relevance alone can return inactive or commercially weak channels. The second pass should check:

last upload date;
videos published in the last 30 and 90 days;
estimated monthly cadence;
recent median and average views;
engagement when public counts make it calculable;
Shorts, long-form, and live ratios;
public business-contact evidence when the brief requires it.

This two-pass design keeps discovery broad enough to find unfamiliar creators while spending enrichment work only on plausible candidates.

Keep the search bounded

Multi-language and multi-country matrices can expand quickly. Use maximumSearchCalls as a global cap, start with 5–10 output channels, and widen one dimension at a time.

If a search returns zero rows, inspect the exclusions and warnings. Requiring a narrow country, high recent views, frequent uploads, a small subscriber band, one format, and a public contact simultaneously can correctly eliminate every candidate.

Try the niche finder Task

The public YouTube Channel Finder by Niche Task starts without mandatory channel URLs and opens with a bounded input you can edit:

Find YouTube channels by topic and market

For the full input and output contract, see YouTube Creator Lead Finder.

The goal is not to search more keywords. It is to keep discovery broad, qualification explicit, and every uncertain attribute honest.

YouTube Channel Scraper in Python: Filter Active Creators

KinderBB — Tue, 25 Aug 2026 00:29:28 +0000

A YouTube channel scraper in Python can collect channel names, subscriber counts, and URLs. That solves extraction. It does not solve creator qualification.

For outreach, the expensive mistake is not missing one channel. It is spending time on channels that stopped publishing, no longer reach viewers, use the wrong content format, or expose no public business-contact route.

This tutorial builds a bounded Python workflow that returns active creator prospects instead of a raw channel dump.

Define active before scraping

“Active” needs a rule. A practical first pass might require:

at least one video per month;
a public upload within the last 60 days;
at least 1,000 median views across a recent sample;
5,000–250,000 subscribers;
long-form or Shorts content that matches the offer.

Those values are not universal. A thumbnail agency, affiliate manager, recruiter, and mobile-app sponsor will use different thresholds. The important part is to write the rule before seeing the results.

Install the Apify client

pip install apify-client

Load your token from an environment variable. Never place it in the script or commit it to Git.

export APIFY_API_TOKEN="..."

Run a qualified channel search

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_API_TOKEN"])

run_input = {
    "preset": "qualified_shortlist",
    "niches": ["AI automation", "B2B SaaS"],
    "languages": ["en"],
    "countries": ["US", "CA", "GB"],
    "worldwideLanguageMode": False,
    "minSubscribers": 5_000,
    "maxSubscribers": 250_000,
    "minVideosPerMonth": 1,
    "lastUploadWithinDays": 60,
    "minRecentMedianViews": 1_000,
    "contentFormats": ["long-form", "Shorts"],
    "requirePublicBusinessContact": False,
    "maxChannels": 10,
    "videosAnalyzedPerChannel": 10,
    "maximumSearchCalls": 12,
}

run = client.actor("kazkn/youtube-creator-lead-finder").call(
    run_input=run_input,
)

dataset = client.dataset(run["defaultDatasetId"])
creators = dataset.list_items().items

The Actor discovers candidates from the requested niches and queries. Channel URLs remain optional. It deduplicates output by channelId inside the run and writes one Dataset row per qualified channel.

Inspect recent performance, not lifetime totals

For each returned creator, the most useful review fields are recent:

for creator in creators:
    print({
        "channel": creator["channelName"],
        "url": creator["channelUrl"],
        "subscribers": creator.get("subscriberCount"),
        "last_upload": creator.get("lastUploadAt"),
        "videos_per_month": creator.get("estimatedVideosPerMonth"),
        "median_views": creator.get("recentMedianViews"),
        "score": creator.get("opportunityScore"),
        "reasons": creator.get("opportunityReasons", []),
        "warnings": creator.get("warnings", []),
    })

Median recent views are especially useful because one viral upload can distort an average. The sample is still bounded, so keep its warnings and do not present the metric as a complete channel history.

Match the content format to the offer

A channel can be active and still be the wrong prospect.

A long-form editing service may prioritize longFormRatio.
A vertical-video agency may prioritize shortsRatio.
A webinar or streaming product may prioritize liveRatio.

The output also includes a format confidence value. Shorts classification uses a duration heuristic from available public API data, so uncertain cases should remain visible rather than being forced into a perfect category.

Keep geography and language evidence separate

Search region, declared country, inferred country, and detected language are different facts.

The Dataset keeps them separate through fields such as:

declaredCountry;
inferredCountries;
countryConfidence;
primaryLanguage;
detectedLanguages;
languageConfidence.

If the brief asks for French-speaking creators worldwide, leave countries empty and set worldwideLanguageMode to True. Do not silently turn the search region into the creator’s declared location.

Export the shortlist to CSV

import csv

columns = [
    "channelId",
    "channelName",
    "channelUrl",
    "subscriberCount",
    "lastUploadAt",
    "estimatedVideosPerMonth",
    "recentMedianViews",
    "shortsRatio",
    "longFormRatio",
    "publicBusinessEmail",
    "contactSourceUrl",
    "opportunityScore",
]

with open("qualified-youtube-creators.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=columns, extrasaction="ignore")
    writer.writeheader()
    writer.writerows(creators)

Keep contactSourceUrl, opportunityReasons, and warnings in the production export. They are the audit trail behind the row.

Public contacts are evidence, not enrichment

The workflow can collect a professional email, website, social profile, or management route only when the creator exposed it in accessible public channel text.

It does not reveal YouTube’s protected business-email field, bypass CAPTCHA, sign in, guess private addresses, or fetch linked websites. An empty contact field is preferable to an invented address.

Start with a bounded public Task

The public Export Qualified YouTube Channels to CSV Task opens with a small editable input and the qualified Dataset view:

Run the CSV-ready YouTube channel scraper

For the complete input schema, output contract, limits, and live pricing, see YouTube Creator Lead Finder.

A useful scraper does not stop when it has extracted the channel. It stops when the row is qualified enough for a human to make the next decision.

Build a YouTube Channel Finder in Python That Qualifies Creators

KinderBB — Mon, 24 Aug 2026 23:58:37 +0000

A basic YouTube channel finder returns channels that match a name or topic. That is useful for lookup. It is not enough for creator outreach.

If you are building a sponsor list, recruiting educators, or selling a service to creators, the real question is narrower:

Which channels match my niche, still publish, reach enough viewers, and expose a public route for business contact?

This tutorial builds that workflow in Python. It starts without a seed list, discovers candidates from a niche, qualifies recent public performance, and returns one structured Dataset row per channel.

The result we want

The input should describe the opportunity, not a pile of channel URLs:

run_input = {
    "preset": "qualified_shortlist",
    "niches": ["AI automation", "B2B SaaS"],
    "languages": ["en"],
    "countries": ["US", "CA", "GB"],
    "worldwideLanguageMode": False,
    "minSubscribers": 5_000,
    "maxSubscribers": 250_000,
    "minVideosPerMonth": 1,
    "lastUploadWithinDays": 60,
    "minRecentMedianViews": 1_000,
    "contentFormats": ["long-form", "Shorts"],
    "requirePublicBusinessContact": False,
    "maxChannels": 10,
    "videosAnalyzedPerChannel": 10,
    "maximumSearchCalls": 12,
}

The output should be reviewable without opening every channel manually. Useful fields include:

channel identity and URL;
subscriber count and recent upload date;
videos published in the last 30 and 90 days;
estimated videos per month;
recent median and average views;
engagement when the public counts make it calculable;
Shorts, long-form, and live ratios;
declared country, inferred countries, and confidence kept separate;
public business email, website, or social route when explicitly exposed;
an opportunity score plus the reasons behind it.

That is the difference between a channel scraper and a creator-prospecting dataset.

Why subscriber count is a weak filter by itself

Subscriber count is easy to retrieve, so many YouTube scraping tutorials stop there. But two channels with 50,000 subscribers can be completely different prospects:

one published yesterday and regularly reaches 8,000 views;
the other last uploaded nine months ago;
one is mostly Shorts while your offer is for long-form editing;
one covers your niche consistently while the other mentioned it once;
one publishes a business email in the channel description while the other does not expose a public contact.

A useful YouTube creator finder therefore has to qualify the recent channel state, not only copy lifetime totals.

Run the Actor from Python

Install the Apify client:

pip install apify-client

Store your Apify token in an environment variable. Do not paste it into source code, a notebook, a screenshot, or a public repository.

export APIFY_API_TOKEN="..."

Then call the Actor and retrieve its default Dataset:

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_API_TOKEN"])

run_input = {
    "preset": "qualified_shortlist",
    "niches": ["AI automation", "B2B SaaS"],
    "languages": ["en"],
    "countries": ["US", "CA", "GB"],
    "worldwideLanguageMode": False,
    "minSubscribers": 5_000,
    "maxSubscribers": 250_000,
    "minVideosPerMonth": 1,
    "lastUploadWithinDays": 60,
    "minRecentMedianViews": 1_000,
    "contentFormats": ["long-form", "Shorts"],
    "requirePublicBusinessContact": False,
    "maxChannels": 10,
    "videosAnalyzedPerChannel": 10,
    "maximumSearchCalls": 12,
}

run = client.actor("kazkn/youtube-creator-lead-finder").call(
    run_input=run_input
)

items = client.dataset(run["defaultDatasetId"]).list_items().items

for creator in items:
    print(
        creator["channelName"],
        creator["channelUrl"],
        creator.get("recentMedianViews"),
        creator.get("opportunityScore"),
    )

The Actor writes one deduplicated row per channelId within the run. Separate runs use separate Datasets, so the same channel can legitimately appear again in a later search.

Search by language worldwide

Country and language are different filters. A French-speaking creator might be based in France, Canada, Belgium, Switzerland, Morocco, or somewhere else entirely.

To find French-speaking creators without forcing one country:

run_input = {
    "preset": "quick_leads",
    "niches": ["AI agents"],
    "languages": ["fr"],
    "countries": [],
    "worldwideLanguageMode": True,
    "maxChannels": 5,
    "videosAnalyzedPerChannel": 5,
    "maximumSearchCalls": 5,
}

The discovery hints guide the search. The output still keeps confirmed language evidence and confidence separate from geography. A search region is not silently presented as a creator’s declared country.

Require activity and recent performance

For outreach, “active” should be explicit. Three inputs do most of the work:

{
    "minVideosPerMonth": 2,
    "lastUploadWithinDays": 45,
    "minRecentMedianViews": 2_000,
}

The Actor samples a bounded number of recent videos per candidate. It calculates cadence over 30 and 90 days, then computes recent median and average views from the available sample.

Median views are especially useful because one viral upload can inflate the average. You should still inspect the sample size and warnings before treating any metric as complete.

Filter by content format

The same niche can contain very different production workflows. A thumbnail agency may want long-form channels. A vertical-video editor may want Shorts-heavy creators. A webinar platform may prefer live streams.

{
    "contentFormats": ["long-form"]
}

The output exposes shortsRatio, longFormRatio, liveRatio, and formatConfidence. Shorts detection from the official API uses a documented duration heuristic, so the Dataset can include a warning instead of pretending the classification is exact.

Treat public contacts as evidence, not enrichment

A YouTube email finder can mean two very different things:

collect a business email the creator explicitly published; or
reveal or guess an address the creator did not expose.

This workflow only does the first.

{
    "preset": "contact_ready",
    "requirePublicBusinessContact": True,
    "contactTypes": ["business_email", "website", "social"],
}

Each returned contact keeps its type, value, source URL, collection time, public status, and confidence. The current provider reads public channel text. It does not access YouTube’s protected business-email field, bypass CAPTCHA, sign in, guess private addresses, or fetch linked websites.

If a protected email is unavailable, that absence is a real result. It should not be replaced by a guessed pattern.

Export to CSV without flattening away the evidence

Apify can export the Dataset directly to CSV, JSON, or Excel. If you need a small CSV for manual review, keep the qualification and provenance columns:

import csv

columns = [
    "channelId",
    "channelName",
    "channelUrl",
    "primaryLanguage",
    "declaredCountry",
    "subscriberCount",
    "lastUploadAt",
    "estimatedVideosPerMonth",
    "recentMedianViews",
    "publicBusinessEmail",
    "contactSourceUrl",
    "opportunityScore",
]

with open("youtube-creators.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=columns, extrasaction="ignore")
    writer.writeheader()
    writer.writerows(items)

Do not drop contactSourceUrl, opportunityReasons, or warnings in the production pipeline. They are what let a human review why a creator qualified and where a contact came from.

Keep the run bounded

YouTube discovery has quota and latency costs. A sensible workflow performs cheap discovery first, then enriches only the best candidates.

Start small:

5–10 qualified channels;
5–10 recent videos checked per channel;
one or two niches;
a bounded search-call budget;
no public-contact requirement until you confirm the niche produces enough candidates.

Then tighten the filters. Requiring a contact, high cadence, high median views, a narrow country, and a small subscriber band all at once can correctly return zero rows.

Try the preconfigured YouTube channel finder

The quickest path is the public YouTube Channel Finder by Niche Task. It opens with a bounded, editable input and the qualified-creator Dataset view:

Run the YouTube Channel Finder by Niche

For all filters, output fields, limits, and live pricing, use the full YouTube Creator Lead Finder.

The useful output is not a longer list. It is a list you can explain, review, and act on.