Youtube Tags or Hashtags Scraper

Youtube Tags or Hashtags Scraper

The Youtube Tags or Hashtags Scraper extracts YouTube video tags and hashtags from public YouTube video pages using one or multiple video URLs. It is designed for users who need structured metadata for content research, SEO analysis, competitive research, dataset creation, or other data workflows.

Provide YouTube video URLs through the startUrls input, run the Actor, and receive one structured result for each successfully processed video. The returned data includes the video URL, thumbnail URL, YouTube tags, and hashtags detected in the video's title and description.

The Actor accepts URLs entered through the request-list style input, including standard youtube.com/watch URLs and supported YouTube URL formats such as youtu.be links.

What Is a Youtube Tags or Hashtags Scraper?

A Youtube Tags or Hashtags Scraper is a data extraction tool focused on collecting metadata associated with public YouTube videos. Instead of manually opening videos and copying tags or hashtags, users can submit a list of video URLs and collect the available information in a structured dataset.

This Actor extracts two related types of metadata:

• YouTube tags returned as tags associated with the video.
• YouTube hashtags detected from the video's title and description.
• Thumbnail information through the available thumbnail URL.
• Original video URL for easy identification and traceability.

The hashtags are identified from the text available in the video title and description. Duplicate hashtags are removed before they are returned.

Key Features

Feature	Description	User Benefit
Multiple YouTube URLs	Process one or more video URLs through startUrls	Collect metadata from several videos in one run
Video tag extraction	Returns the tags associated with each processed video	Useful for keyword and content research
Hashtag extraction	Detects hashtags in video titles and descriptions	Helps analyze hashtag usage and topical themes
Array and text fields	Tags and hashtags are provided in both array and text forms	Convenient for analysis and downstream processing
Thumbnail URL	Returns an available thumbnail URL	Makes it easier to associate visual assets with results
Structured dataset results	Each successful video produces a separate structured output item	Simplifies filtering, analysis, and dataset creation
Public video workflow	Designed for public YouTube video URLs	Suitable for research based on publicly available video pages
Cookie-free operation	The Actor description specifies operation without cookies	Reduces the need for users to provide browser cookies

What Data Can You Extract?

The Youtube Tags or Hashtags Scraper returns a compact set of video metadata centered on tags and hashtags.

Video identification

Each result includes the YouTube URL supplied for processing. This allows users to connect extracted metadata with the original source video.

Thumbnail information

The Actor returns a thumbnailUrl value when thumbnail information is available. When multiple thumbnail resolutions are available, it selects the highest available thumbnail resolution that includes a height value.

YouTube tags

The tagsArray field contains the tags returned for the video. The Actor also creates tagsText, which joins the available tags into a comma-separated string.

YouTube hashtags

The hashtagsArray field contains hashtags detected in the video's title and description. The Actor removes duplicates and sorts the resulting values. The hashtagsText field provides the hashtags as a space-separated string.

Why Use This Actor?

Manual collection of video tags and hashtags becomes repetitive when working with many YouTube videos. This Actor provides a simple URL-based workflow for collecting the same categories of information in a structured form.

It can be useful when you need to:

• Analyze keyword and hashtag patterns across selected videos.
• Build datasets for YouTube content research.
• Compare metadata across a group of public videos.
• Study how creators use tags and hashtags around specific topics.
• Prepare structured metadata for further analysis or reporting.

Because the workflow is URL-based, users can select the exact videos they want to investigate rather than relying on a general keyword search.

Benefits

The Actor provides several practical benefits for metadata collection.

Automation

Instead of opening each video and manually recording tags and hashtags, users can submit multiple URLs and process them within one Actor run.

Structured results

Tags and hashtags are returned as arrays as well as text fields. This makes the resulting dataset easier to inspect and process in different workflows.

Research-focused extraction

The extracted fields are suitable for content research, topic discovery, keyword analysis, and comparative metadata studies.

Simple input model

The required input is a startUrls list containing YouTube video URLs. There are no additional configuration fields in the supplied input schema.

Duplicate hashtag handling

Repeated hashtags detected in the title and description are consolidated into a single hashtag entry.

How to Use the Youtube Tags or Hashtags Scraper

Using the Actor requires only a list of YouTube video URLs.

1. Open the Actor and locate the Start URLs input.
2. Add one or more public YouTube video URLs.
3. Start the Actor.
4. Review the structured results in the dataset.
5. Use the returned tags, hashtags, thumbnail URLs, and video URLs in your research or data workflow.

For initial testing, use a small number of video URLs so you can confirm that the returned fields match your intended workflow before processing a larger URL list.

Input

The Actor has one required input field: startUrls.

Field	Type	Required	Description
startUrls	Array	Yes	One or more YouTube video URL entries to process

The input is configured with a request-list style editor. Each list entry can contain a url value. The implementation also accepts string values, although the interface is designed around URL objects.

The supplied actor.json does not define a numeric maximum for the number of URLs in startUrls, so no additional URL-count limit is documented here.

Input Example

{
  "startUrls": [
    {
      "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
    },
    {
      "url": "https://youtu.be/9bZkp7q19f0"
    }
  ]
}

Use actual public YouTube video URLs when running the Actor. Avoid adding unsupported fields because they are not part of the documented input schema.

Output

Each successfully processed video produces one structured dataset item.

The output focuses on video identification, thumbnail information, tags, and hashtags. Tags come from the video's available tag metadata, while hashtags are detected from the title and description.

Field	Description
youtubeVideoUrl	The YouTube video URL processed by the Actor
thumbnailUrl	Available thumbnail URL selected for the processed video
tagsArray	Array of tags returned for the video
tagsText	Tags joined into a comma-separated text string
hashtagsArray	Unique hashtags detected in the video title and description
hashtagsText	Hashtags joined into a space-separated text string

A video that cannot be processed successfully is not added as a successful output item. The Actor continues through the supplied URL list rather than requiring every submitted URL to produce a result.

Output Example

{
  "youtubeVideoUrl": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
  "thumbnailUrl": "https://example.com/thumbnail.jpg",
  "tagsArray": [
    "music",
    "official video",
    "pop"
  ],
  "tagsText": "music, official video, pop",
  "hashtagsArray": [
    "#Music",
    "#OfficialVideo",
    "#Pop"
  ],
  "hashtagsText": "#Music #OfficialVideo #Pop"
}

The example values above illustrate the output structure. Actual tags, hashtags, and thumbnail URLs depend on the processed video.

Use Cases

The Youtube Tags or Hashtags Scraper can support several research and data-collection workflows.

YouTube SEO research

Collect tags and hashtags from selected public videos to study keyword and topic patterns used by creators.

Content research

Researchers can compare metadata across videos covering similar subjects and identify recurring terminology.

Competitive research

Marketing and content teams can examine metadata from selected competitor or industry videos to understand how publicly visible video metadata is organized.

Dataset creation

Create structured video metadata datasets for further analysis, reporting, categorization, or internal research.

Hashtag analysis

Collect hashtags appearing in video titles and descriptions to investigate topical relationships and recurring hashtag usage.

Content planning

Writers and creators can use collected metadata as research material when studying how existing videos describe similar topics.

Advantages

The Actor combines several related metadata fields into a single dataset row, making it useful for workflows that need both tag and hashtag information.

It also provides array and text versions of tags and hashtags. Arrays are useful when individual values need to be filtered or analyzed, while text fields can be more convenient for quick review or downstream text processing.

The URL-based workflow also keeps the target set explicit: users decide which public YouTube videos should be analyzed by supplying their URLs.

Limitations

The Actor is focused specifically on public YouTube video URLs supplied through startUrls. It is not documented as a general YouTube search scraper, channel scraper, playlist scraper, or comment scraper.

Hashtags are extracted from the video title and description using hashtag matching. They are not described as coming from a separate hashtag search index.

Output availability can vary between videos. For example, a video may have no tags, no detectable hashtags, or unavailable thumbnail information. In those cases, corresponding fields can be empty.

The supplied implementation does not define a custom maximum URL count in actor.json, but very large input lists may still require appropriate run planning and validation.

Pros and Cons

Pros	Cons
Processes multiple video URLs	Focused on video URLs rather than general YouTube discovery
Returns tags and hashtags together	Hashtags are detected specifically from title and description text
Provides array and text formats	Some videos may not provide all metadata
Includes thumbnail URL information	No separate input options for advanced filtering
Simple required input structure	Does not document broader YouTube data such as comments or channel datasets

Comparison With Alternative Approaches

Capability	This Actor	Manual / Typical Alternative
Process multiple selected video URLs	Supported through startUrls	Often requires opening videos individually
Structured tags	Returned as tagsArray and tagsText	May require manual copying and formatting
Structured hashtags	Returned as hashtagsArray and hashtagsText	Usually collected manually from visible text
Thumbnail URL	Returned when available	May require separate manual collection
Duplicate hashtag handling	Duplicate detected hashtags are consolidated	Manual cleanup may be required
Dataset-oriented workflow	Output is pushed as structured dataset records	Manual research often needs additional formatting

This comparison describes workflow differences rather than guaranteeing that every manual or alternative approach behaves in exactly the same way.

Best Practices

For reliable and useful results:

• Start with a small group of public YouTube video URLs and review the dataset.
• Use valid video URLs and keep the startUrls structure consistent.
• Check both array and text fields to determine which format best suits your workflow.
• Treat empty tag or hashtag fields as valid possible outcomes rather than assuming every video contains metadata.
• Validate important extracted information before using it in business-critical research or analysis.
• Keep the original youtubeVideoUrl alongside extracted metadata so results remain easy to trace back to their source.

Troubleshooting

Invalid or unsuitable input

Check that each entry in startUrls contains a valid YouTube video URL. Remove blank entries or malformed values before starting the run.

No result for a video

A submitted video may fail to produce information. Review the URL and confirm that the target is a public YouTube video page. The Actor skips inputs for which usable video information is not obtained.

Empty tags

Some videos may not return any tags. In this case, tagsArray can be empty and tagsText can contain an empty string.

No hashtags

Hashtags are detected from the title and description. A video without matching hashtag text can therefore produce an empty hashtagsArray and hashtagsText.

Missing thumbnail information

Thumbnail availability can differ by video. The Actor returns an available thumbnail URL when it can obtain one.

Partial results

When processing multiple URLs, some videos may succeed while others do not. Review the dataset against the original URL list when completeness is important.

Frequently Asked Questions

What does the Youtube Tags or Hashtags Scraper do?

The Youtube Tags or Hashtags Scraper processes supplied public YouTube video URLs and returns video tags, hashtags, thumbnail information, and the original video URL in structured records.

What input does the Actor require?

It requires the startUrls array. Each entry should provide a YouTube video URL.

Can I process multiple YouTube videos?

Yes. startUrls is an array, so multiple video URLs can be submitted in the same run.

Where are the hashtags collected from?

The Actor detects hashtags in the video's title and description. Detected duplicates are removed before the results are returned.

How are YouTube tags returned?

Tags are available in tagsArray for individual values and tagsText as a comma-separated string.

How are hashtags returned?

Hashtags are provided through hashtagsArray and hashtagsText. The array contains unique, sorted hashtag values detected from the title and description.

Does the Youtube Tags or Hashtags Scraper return thumbnails?

Yes. The thumbnailUrl field contains an available thumbnail URL when thumbnail information is obtained.

What happens when a video has no tags or hashtags?

The related fields can be empty. The presence of an empty field does not necessarily indicate an input problem.

Can this Actor search YouTube by keyword?

The supplied input schema is based on video URLs. It is not documented as a keyword-based YouTube search scraper.

Is this Actor useful for automation?

Yes. Its URL-list input and structured dataset output make it suitable for repeatable research, metadata collection, and data-processing workflows where the target videos are already known.

NLP Keywords

• YouTube video metadata
• YouTube tags
• YouTube hashtags
• video tag extraction
• hashtag extraction
• YouTube video scraper
• public YouTube videos
• video metadata scraper
• YouTube SEO data
• video keyword research
• YouTube content research
• structured video data
• YouTube dataset creation
• video title analysis
• video description analysis
• hashtag metadata
• tag metadata
• YouTube thumbnail URL
• video metadata collection
• YouTube research data

Related Keywords

• YouTube tags scraper
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• YouTube video tag extractor
• YouTube video hashtag extractor
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• YouTube metadata extraction
• YouTube keyword metadata
• YouTube SEO tags research
• YouTube hashtag research tool
• YouTube video metadata extractor
• YouTube video data collection
• collect YouTube tags
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• YouTube video research scraper
• YouTube metadata dataset

Final Overview

The Youtube Tags or Hashtags Scraper provides a focused way to collect tag and hashtag metadata from selected public YouTube videos. Users submit one or more video URLs, and the Actor returns structured records containing the source URL, thumbnail URL, tags, and hashtags.

Its straightforward input structure and dataset-oriented output make it suitable for YouTube SEO research, content analysis, metadata collection, competitive research, and structured dataset creation. Because the Actor documents a focused set of outputs, users can quickly understand what information will be collected and incorporate those results into their existing research workflow.

Contact me: Alphascraper69@gmail.com