Youtube Channel Video Scraper

YouTube Channel Video Scraper

The YouTube Channel Video Scraper extracts structured video metadata from YouTube channels and stores each discovered video as a separate result. It is designed for users who need channel video data for research, content analysis, dataset creation, monitoring, analytics, or automated workflows.

You can provide one or more channel usernames or handles, channel IDs, channel URLs, or search keywords. The Actor combines the supplied inputs, removes duplicate channel targets, and processes up to the configured number of videos from each identified channel.

For each video, the scraper can return core metadata such as the channel name, video ID, title, description, video URL, publication information, duration, view count, like count, comment count, thumbnail URL, available subtitle languages, tags, hashtags, categories, and live-status information. Optional settings can additionally return video formats, audio formats, and all available thumbnails.

What Is the YouTube Channel Video Scraper?

The YouTube Channel Video Scraper is a channel-focused data extraction Actor for collecting video information from identified YouTube channels.

Instead of manually opening individual channel pages and recording video details, users provide channel-oriented inputs and receive structured records in the Actor dataset. This makes the collected information easier to inspect, analyze, and reuse in downstream workflows.

The Actor accepts several input paths:

• Keywords for channel discovery
• Usernames or handles such as MrBeast or @MrBeast
• Channel IDs beginning with UC
• Start URLs containing YouTube channel links or supported channel URL forms

Multiple values can be supplied within these input types. The Actor combines the resulting channel targets and avoids processing the same normalized channel more than once.

Key Features

Feature	Description	User Benefit
Multiple channel inputs	Accepts keywords, usernames/handles, channel IDs, and start URLs	Flexible channel data collection
Duplicate channel handling	Normalized channel targets are compared before processing	Reduces duplicate channel processing
Configurable video limit	Set the maximum number of videos processed per channel	Control dataset size
Detailed video metadata	Collects titles, descriptions, dates, counts, URLs, duration, and more	Useful for research and analysis
Engagement metadata	Includes tags, hashtags, categories, age-related fields, and audience-related metadata when available	Supports content analysis
Optional video formats	Can include detailed video format records	Useful for technical video metadata research
Optional audio formats	Can include available audio-only format information	Adds media-format detail when needed
Optional thumbnails	Can include all available thumbnail variants	Useful for thumbnail and media analysis
Structured dataset output	Each processed video is pushed as an individual result	Easy to inspect and analyze

What Data Can You Extract?

Each video result contains a structured set of fields covering content, publication, engagement, media, and technical metadata.

Video and Content Data

The main video record includes:

• channel_name
• video_id
• video_url
• title
• description
• description_links
• playback_url
• published_at
• upload_date

The description is preserved as returned by the source, while description_links contains HTTP or HTTPS links detected within that description.

Duration and Status

The Actor also returns:

• duration_seconds
• duration_string
• video_type
• is_live
• was_live

This provides both numeric and human-readable duration information alongside live-stream status indicators.

Engagement and Discovery Metadata

The engagement object may contain:

• tags
• hashtags
• categories
• age_limit
• is_made_for_kids

Hashtags are extracted from the video description. The other values are populated from available video metadata.

Counts and Media

The result includes formatted values for:

• view_count
• like_count
• total_comments
• thumbnail_url
• available_languages

The count fields are formatted using compact values such as 1.2k, 4.5m, or 1.1b where applicable.

Optional Format and Thumbnail Data

When enabled in the input configuration, additional fields can be returned.

video_formats may contain format ID, extension, resolution, frame rate, file size, video codec, audio codec, and a video URL.

audio_formats may contain format ID, extension, bitrate, file size, audio codec, and an audio URL.

thumbnails may contain multiple thumbnail URLs together with their width and height.

Why Use This Actor?

Manually collecting video metadata from multiple channels can become repetitive, especially when the same information is needed across many channels.

This YouTube Channel Video Scraper turns channel-oriented inputs into a structured video dataset that can support:

• Automated content research
• Channel and video metadata collection
• Competitive content analysis
• Market and audience research
• Video dataset creation
• Content discovery workflows
• Metadata comparison
• Media-format research
• Reporting and analytics workflows

The ability to combine several input types is particularly useful when your source list contains a mixture of handles, channel IDs, URLs, and search terms.

Benefits for Data Collection and Research

The Actor is useful when the objective is to collect consistently structured video records rather than manually copying individual details.

Because each video is written as a separate dataset item, users can work with individual records while preserving related metadata such as the title, description, publication date, engagement counts, and content classification fields.

Optional format and thumbnail extraction also allows the same Actor to support both general metadata collection and more detailed media-data research, depending on the configuration.

How to Use the YouTube Channel Video Scraper

The workflow is straightforward:

1. Provide one or more supported channel inputs.
2. Set the maximum number of videos to process from each channel.
3. Enable video formats, audio formats, or thumbnails when those fields are required.
4. Start the Actor.
5. Review the resulting structured video dataset.

You do not need to provide every input type. Use the input method that best matches your channel list.

For larger collections, start with a small maxposts value and verify the returned dataset before expanding the run.

Input Fields

Field	Type	Required	Default	Description
keywords	Array of strings	No	—	Search keywords used for YouTube channel discovery. Examples include cooking or tech reviews.
usernames	Array of strings	No	—	YouTube usernames or handles, with or without @.
channelIds	Array of strings	No	—	YouTube channel IDs, normally beginning with UC.
startUrls	Array of objects	No	—	One or more YouTube channel URLs or supported channel/share links. Each object contains a required url field.
maxposts	Integer	No	5 in actor configuration	Maximum number of videos processed per channel.
get_video_formats	Boolean	No	false	Adds detailed video format information to each result.
get_audio_formats	Boolean	No	false	Adds detailed audio-only format information to each result.
get_thumbnails	Boolean	No	false	Adds a list of available thumbnails with dimensions.

The Actor combines populated input collections and attempts to normalize channel targets before processing them. Duplicate normalized channel targets are skipped.

Input Example

{
  "keywords": [
    "MrBeast",
    "technology reviews"
  ],
  "usernames": [
    "@loganpaulvlogs"
  ],
  "channelIds": [
    "UC-lHJZR3Gqxm24_Vd_AJ5Yw"
  ],
  "startUrls": [
    {
      "url": "https://www.youtube.com/@PenMovies/"
    },
    {
      "url": "https://www.youtube.com/@PlayDMF/"
    }
  ],
  "maxposts": 5,
  "get_video_formats": false,
  "get_audio_formats": false,
  "get_thumbnails": false
}

Output

The Actor writes one structured result for each processed video.

The core output is intentionally centered around video metadata, publication details, engagement information, and source-related metadata.

Output Fields

Field	Description
channel_name	Channel name associated with the video.
video_id	YouTube video identifier.
video_url	Video webpage URL.
title	Video title.
description	Video description.
description_links	HTTP or HTTPS links detected in the description.
playback_url	Video URL used as the playback reference.
published_at	Publication timestamp derived from the upload date when available.
upload_date	Upload date in YYYYMMDD form when available.
duration_seconds	Video duration in seconds.
duration_string	Human-readable duration.
video_type	Video media extension/type value.
is_live	Indicates whether the entry is identified as live.
was_live	Indicates whether the video was previously live.
view_count	Formatted view count.
like_count	Formatted like count.
total_comments	Formatted comment count.
thumbnail_url	Primary available thumbnail URL.
available_languages	Available subtitle language keys when present.
engagement	Object containing tags, hashtags, categories, age limit, and made-for-kids metadata.
technical	Technical source metadata including webpage URL, original URL, extractor, and extractor version.
video_formats	Optional detailed video format list when enabled.
audio_formats	Optional detailed audio-only format list when enabled.
thumbnails	Optional list of thumbnail variants with URLs and dimensions when enabled.

Output Example

{
  "channel_name": "Example Channel",
  "video_id": "dQw4w9WgXcQ",
  "video_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "title": "Example Video Title",
  "description": "Example video description with https://example.com",
  "description_links": [
    "https://example.com"
  ],
  "playback_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "published_at": "2026-01-15T00:00:00",
  "upload_date": "20260115",
  "duration_seconds": 600,
  "duration_string": "00:10:00",
  "video_type": "mp4",
  "is_live": false,
  "was_live": false,
  "view_count": "1.2m",
  "like_count": "45.6k",
  "total_comments": "3.4k",
  "thumbnail_url": "https://example.com/thumbnail.jpg",
  "available_languages": [
    "en"
  ],
  "engagement": {
    "tags": [
      "technology"
    ],
    "hashtags": [
      "Tech"
    ],
    "categories": [
      "Science & Technology"
    ],
    "age_limit": 0,
    "is_made_for_kids": false
  },
  "technical": {
    "webpage_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "original_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "extractor": "youtube",
    "extractor_version": "example"
  }
}

The example uses placeholder values to illustrate the structure. Actual metadata depends on the video being processed.

Use Cases

The collected video dataset can support several practical workflows.

Content research: Analyze titles, descriptions, tags, hashtags, categories, and publication dates across selected channels.

Competitive research: Compare video publishing patterns and engagement metadata across channel datasets.

Dataset creation: Build structured collections of channel videos for analytics, research, or internal data projects.

Media research: Use optional format and thumbnail information when studying available video or audio representations.

Channel monitoring: Re-run the Actor periodically to collect a configurable number of recent channel videos for comparison over time.

Business intelligence: Combine video metadata and engagement indicators with other research datasets for broader analysis.

Academic research: Use structured channel video records as source material for studies involving online video content and publishing activity.

Advantages

The main strengths of this Actor are its flexible input model and configurable output depth. A user can start with a simple handle or URL, while more advanced workflows can provide channel IDs, multiple channels, or search-oriented keywords.

The output also combines core video metadata with nested engagement and technical information. Optional media fields can be enabled only when they are relevant to the task, helping users choose between a simpler metadata dataset and a more detailed result.

Limitations

Several practical limitations should be considered:

• Keyword-based discovery depends on finding a channel from the resulting YouTube search information. A keyword does not represent an exact channel identifier.
• The number of processed videos is controlled per channel through maxposts.
• Some metadata fields may be unavailable for particular videos, so values can be empty, missing, or represented by defaults.
• Video, audio, and thumbnail details are optional and are only added when their corresponding switches are enabled.
• A failed or unavailable channel may produce no usable video records for that target.
• The Actor processes identified channel targets; it is not documented as a general-purpose search-results scraper for arbitrary YouTube search pages.

Pros and Cons

Pros	Cons
Supports multiple channel input methods	Keyword discovery may not identify the intended channel in every case
Combines multiple inputs in one run	Maximum videos are controlled per channel
Removes duplicate normalized channel targets	Some source metadata may be unavailable
Provides structured video records	Optional detailed fields increase output size
Supports optional video, audio, and thumbnail data	Results depend on available source metadata

Comparison With Alternative Approaches

Capability	This Actor	Manual / Typical Alternative
Multiple channel inputs	Supported	Often handled one source at a time
Automated video metadata collection	Supported	Usually requires repeated manual collection
Structured dataset records	Supported	Often requires manual formatting
Configurable videos per channel	Supported through maxposts	Usually controlled manually
Optional format metadata	Supported when enabled	Requires additional manual inspection
Optional thumbnail collection	Supported when enabled	Often requires collecting variants separately
Duplicate channel handling	Supported for normalized targets	Usually requires manual deduplication

This comparison describes workflow differences rather than a universal performance ranking.

Competitive Advantages

The Actor provides a practical combination of channel discovery, channel identification, configurable video limits, and structured metadata output.

Its flexible input model is useful when a project does not have one standardized source list. Handles can be mixed with channel IDs and URLs, while keyword-based discovery provides another way to identify channels.

The optional fields are also useful for tailoring the dataset. A metadata-focused run can leave media-detail options disabled, while a more technical research workflow can request format or thumbnail information.

Best Practices

• Start with a small number of channels and a low maxposts value to confirm that your inputs resolve to the expected targets.
• Use direct channel URLs or channel IDs when you already know the exact channels you need.
• Use usernames with or without @; the Actor normalizes the handle for processing.
• Enable get_video_formats, get_audio_formats, or get_thumbnails only when those fields are necessary for your analysis.
• Review the resulting dataset before running a larger collection.
• Treat source-dependent fields such as counts, tags, subtitles, or categories as potentially variable.
• Validate important records before using them in critical research or reporting.

Troubleshooting

Invalid or unexpected input: Check that channel handles, channel IDs, and URLs use the documented forms. For startUrls, every array item must contain a url field.

Empty results: Verify that the supplied target identifies a valid YouTube channel and try a direct channel URL or channel ID when available.

Partial results: Some videos may not provide complete metadata, and individual channel targets may fail to return usable information.

Missing optional fields: Confirm that the corresponding setting is enabled. video_formats, audio_formats, and thumbnails are only included when requested.

Unexpected keyword results: Keywords are used for channel discovery, so the discovered channel should be reviewed before treating the resulting dataset as a specific-channel collection.

Temporary failures: Recheck the input and run the Actor again. For important datasets, review the returned records rather than assuming every requested channel produced the same number of items.

Frequently Asked Questions

What does the YouTube Channel Video Scraper do? It collects structured metadata for videos from identified YouTube channels and writes each video as a separate dataset result.

What inputs does the YouTube Channel Video Scraper support? It supports arrays of keywords, usernames or handles, channel IDs, and start URLs.

Can I provide multiple channels in one run? Yes. Multiple values can be supplied across the supported input fields, and normalized duplicate channel targets are skipped.

What is maxposts? maxposts controls the maximum number of videos processed from each channel. The actor configuration specifies a default value of 5.

Can I use a YouTube channel URL directly? Yes. startUrls accepts YouTube channel URLs and supported channel URL forms. The Actor normalizes channel targets before processing.

Can keywords be used instead of channel IDs? Yes. Keywords can be supplied for channel discovery. Because this is discovery-based, users should verify that the identified channel is the intended target.

Does the scraper return video descriptions and links? Yes. The output includes the full description and a separate list of HTTP or HTTPS links detected in that description.

Can I collect video and audio format information? Yes. Enable get_video_formats and/or get_audio_formats to add those corresponding output fields.

Can I collect all available thumbnails? Yes. Set get_thumbnails to true to add the thumbnail list with URL, width, and height information.

What happens when metadata is unavailable? Some fields can be empty, unavailable, or represented by defaults depending on the source data returned for a particular video.

Is the YouTube Channel Video Scraper suitable for automation? Yes. The Actor produces structured per-video records that can be reused in automated research, dataset-building, monitoring, and analysis workflows.

NLP Keywords

• YouTube channel video data
• YouTube video metadata
• YouTube channel scraper
• channel video extraction
• YouTube video information
• channel video dataset
• YouTube content metadata
• video title extraction
• YouTube description data
• video engagement metadata
• YouTube view count
• YouTube like count
• YouTube comment count
• YouTube thumbnail data
• YouTube video formats
• YouTube audio formats
• YouTube channel IDs
• YouTube channel handles
• YouTube video research
• structured YouTube data

Related Keywords

• YouTube channel data scraper
• YouTube video scraper
• scrape YouTube channel videos
• YouTube channel metadata extractor
• YouTube video metadata scraper
• extract YouTube video details
• YouTube channel video extractor
• YouTube channel content scraper
• YouTube video dataset builder
• YouTube video information extractor
• channel video data collection
• YouTube content research scraper
• YouTube video analytics data
• YouTube thumbnail extractor
• YouTube video format extractor
• YouTube audio format scraper
• YouTube channel URL scraper
• YouTube handle scraper
• YouTube channel ID scraper
• automated YouTube video data extraction

Final Overview

The YouTube Channel Video Scraper provides a flexible way to turn channel-oriented YouTube inputs into structured video data. It supports keywords, handles, channel IDs, and channel URLs, combines multiple targets, removes duplicate normalized channels, and limits collection per channel with maxposts.

Its standard output covers video identity, titles, descriptions, publication data, durations, engagement counts, thumbnails, subtitle-language availability, hashtags, categories, and other metadata. Optional configuration adds video formats, audio formats, and thumbnail variants when those details are required.

For content research, competitive analysis, dataset creation, channel monitoring, media research, and automated workflows, the Actor provides a clear input-to-output process: provide channel targets, configure the collection depth, start the Actor, and work with the resulting structured video dataset.

Contact me: Alphascraper69@gmail.com