Scraping Youtube Trending Videos

Raw data collection. No assumed use case — returns the full dataset for downstream analysis.

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Inputs

Parameter	Type	Required	Default	Notes
type	string	Optional	n	Trending category: n (now), music, movies, gaming
gl	string	Optional	us	Country code (e.g. US, GB)
hl	string	Optional	en	Language code (e.g. en)
maxItems	number	Optional	Unlimited	Maximum videos to return
customMapFunction	string	Optional	—	JavaScript function to transform each output object

How to Run

Using run_actor.js (recommended)

# Quick answer (table)
node scripts/run_actor.js --actor "apidojo~youtube-trending-scraper" --input '{"type": "n", "gl": "us", "hl": "en", "maxItems": 50}'

# Save as CSV
node scripts/run_actor.js --actor "apidojo~youtube-trending-scraper" --input '{"type": "n", "gl": "us", "hl": "en", "maxItems": 50}' --output results.csv --format csv

# Save as JSON
node scripts/run_actor.js --actor "apidojo~youtube-trending-scraper" --input '{"type": "n", "gl": "us", "hl": "en", "maxItems": 50}' --output results.json --format json

REST API fallback

curl -X POST "https://api.apify.com/v2/acts/apidojo~youtube-trending-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"type": "n", "gl": "us", "hl": "en", "maxItems": 50}'

If Apify MCP is available: Use the Apify MCP call_actor tool with actor apidojo~youtube-trending-scraper and the input above.

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Output Fields

Field	Type	Description
type	string	Always video
id	string	Video ID
title	string	Video title
url	string	Video URL
description	string	Video description
duration	number	Duration in seconds
views	number	View count
likes	number	Like count
status	string	Availability status
channel.id	string	Channel ID
channel.name	string	Channel name
channel.url	string	Channel URL
keywords	array	Video tags/keywords
isLive	boolean	Live stream flag
thumbnails	array	Thumbnail objects

Edge Cases

• No results for category: Some countries may not have all category pages. Fall back to type=n.
• Empty results: YouTube may show different trending per session — retry.
• Region mismatch: gl and hl must be valid ISO codes.
• Data freshness: Trending changes frequently — data reflects a point-in-time snapshot.