Youtube Shorts Comments Scraper

Youtube Shorts Comments Scraper

The Youtube Shorts Comments Scraper extracts comments from one or more YouTube Shorts video URLs and returns them as structured data. It is intended for audience research, content analysis, dataset creation, and other workflows that need organized Shorts comment data.

Provide one or more Shorts URLs through startUrls, choose whether to collect all available comments or apply a maximum comment limit, and configure the available comment-order option. For each video, the Actor returns the URL, video ID, title when available, comment count, scraped count, and comment records with text and metadata.

Results are pushed to the Apify dataset as a structured record for each submitted video, keeping the video and its collected comments together.

What Is a Youtube Shorts Comments Scraper?

This Actor extracts comments from selected YouTube Shorts videos using direct URLs rather than channel or keyword discovery.

The input supports one or more URLs in a request-list style array. Users can control comment volume with maxComments, or enable getAllComments to continue collecting comments without applying the configured maximum.

Key Features

Feature	Description	User Benefit
YouTube Shorts URL input	Accepts one or more URLs through startUrls	Process selected Shorts in one run
Comment extraction	Collects comments associated with each submitted video	Build a comment dataset
Comment limit	maxComments can limit collection	Control returned data volume
All-comments mode	getAllComments can request all available comments	Collect a broader comment set
Structured output	Returns video and normalized comment records	Easier downstream processing
Video identification	Includes videoId and videoUrl	Keep comments linked to the source
Comment metadata	Includes author, time, replies, and votes when available	Add context for analysis
Sort configuration	The input exposes top and newest comment choices	Configure the intended collection order

What Data Can You Extract?

The Youtube Shorts Comments Scraper returns video-level information and an array of normalized comment records.

Video-level fields include:

• videoUrl — the submitted YouTube URL.
• videoId — the detected YouTube video identifier when available.
• title — the video title when it can be obtained.
• commentsCount — a detected comment count, with a fallback to the number actually scraped.
• comments — the collected comment records.
• scrapedCount — the number of comments included in the result.

Each comment record can include:

• cid — comment identifier when available.
• type — the normalized value comment.
• publishedTimeText — original relative published-time text when available.
• publishedTimeTs — an estimated Unix timestamp when the time text can be parsed.
• comment — comment text.
• author — comment author when available.
• replyCount — normalized reply count.
• voteCount — normalized vote count.
• videoId — source video ID.
• pageUrl — source video URL.

publishedTimeTs is derived from relative time text. Month and year values are approximated, so it should be treated as an estimate rather than an exact source timestamp.

Why Use This Actor?

The main benefit of the Youtube Shorts Comments Scraper is automated collection from selected video URLs. Instead of manually copying comments from several Shorts, users can provide the URLs and receive structured records in an Apify dataset.

It is useful when you need to:

• Collect comments from multiple Shorts in a repeatable workflow.
• Create a dataset for audience or content research.
• Review comment text together with authors, votes, and replies.
• Keep every comment connected to its source video.
• Limit collection to a manageable amount of data.
• Request all available comments when broader collection is needed.

Benefits

Automation reduces repetitive manual collection across multiple Shorts.

Structured data gives you a consistent JSON-style result instead of manually assembled notes.

Flexible collection volume lets you use a positive maximum or request all available comments.

Multiple URL support makes batch research possible from a list of selected videos.

Comment metadata adds context beyond comment text, including author, reply count, vote count, and published-time information when available.

How to Use the Youtube Shorts Comments Scraper

1. Add one or more YouTube Shorts URLs to startUrls.
2. Set maxComments when you want a comment limit.
3. Set getAllComments to true when you want all available comments instead of stopping at the maximum.
4. Select a sortBy option.
5. Start the Actor run.
6. Review the resulting Apify dataset items.

Input

The Actor accepts one required input and three optional configuration fields.

Field	Type	Required	Default / Prefill	Description
startUrls	Array	Yes	—	One or more YouTube Shorts URLs
maxComments	Integer	No	100 prefilled	Maximum comments to scrape when all-comments mode is disabled
getAllComments	Boolean	No	false	Request all available comments regardless of maxComments
sortBy	String	No	top comments	Exposed choice for top or newest comments

startUrls uses a request-list format where each item contains a url.

A positive maxComments value is recommended when you want controlled result volume.

When getAllComments is true, the maximum comment setting is ignored by the scraping loop.

Input Example

{
  "startUrls": [
    {
      "url": "https://www.youtube.com/shorts/z8JfN67sXTY"
    },
    {
      "url": "https://youtube.com/shorts/k3Mm28-CqwQ?si=f2xA5k55E5srjnu3"
    }
  ],
  "maxComments": 100,
  "getAllComments": false,
  "sortBy": "top comments"
}

For all-comments mode:

{
  "startUrls": [
    {
      "url": "https://www.youtube.com/shorts/z8JfN67sXTY"
    }
  ],
  "getAllComments": true,
  "sortBy": "newest comments"
}

Output

The Youtube Shorts Comments Scraper returns one result object for each submitted video URL. Each object is pushed to the Apify dataset.

The top-level structure contains video details and a comments array. This keeps all collected comments associated with the video that produced them.

Field	Description
videoUrl	Processed YouTube URL
videoId	Extracted video ID when available
title	Video title when available
commentsCount	Detected comment count or scraped-count fallback
comments	Array of normalized comment records
scrapedCount	Number of comments in the comments array

Output Example

{
  "videoUrl": "https://www.youtube.com/shorts/z8JfN67sXTY",
  "videoId": "z8JfN67sXTY",
  "title": "Example YouTube Short",
  "commentsCount": 100,
  "comments": [
    {
      "cid": "comment-id-example",
      "type": "comment",
      "publishedTimeText": "2 days ago",
      "publishedTimeTs": 1789545600.0,
      "comment": "This Short is very useful.",
      "author": "Example User",
      "replyCount": 3,
      "voteCount": 25,
      "videoId": "z8JfN67sXTY",
      "pageUrl": "https://www.youtube.com/shorts/z8JfN67sXTY"
    }
  ],
  "scrapedCount": 1
}

The example shows the documented structure. Individual results may contain null values where source information is unavailable.

Use Cases

Audience research: examine viewer comments, authors, votes, and replies to understand reactions to Shorts.

Content research: identify recurring topics, questions, reactions, and discussion themes in selected videos.

Competitive research: compare comments and engagement-related fields across a chosen set of Shorts.

Dataset creation: build structured collections of YouTube Shorts comments for later filtering or analysis.

Engagement analysis: combine comment text with replyCount and voteCount for additional context.

Research automation: process multiple direct video URLs without copying each comment manually.

Advantages

Advantage	How it helps
Direct Shorts targeting	Start from specific video URLs
Multiple inputs	Process several videos in one run
Configurable volume	Use a maximum or all-comments mode
Rich comment records	Capture text with useful metadata
Video context	Retain source URL and video ID
Dataset-ready structure	Work directly with an Apify dataset

Limitations

The Actor is focused on comments from selected YouTube video URLs. It does not provide documented controls for channel discovery, keyword search, playlist discovery, or automatic video selection.

Some metadata is best-effort. A video title may be null, and commentsCount can fall back to the number of comments actually scraped when a page-level count cannot be determined.

publishedTimeTs is estimated from relative-time text. The implementation approximates months as 30 days and years as 365 days, so this value should not be treated as an exact publication timestamp.

The current input displays top comments and newest comments, while the runtime sorting logic checks for the shorter values top and newest. Because of this mismatch, the selected sort option may not be applied as intended in the current version.

Pros and Cons

Pros	Cons
Accepts multiple YouTube Shorts URLs	No documented video-discovery feature
Supports a maximum comment limit	All-comments mode can return much more data
Provides structured video and comment records	Some metadata may be unavailable
Includes author, votes, replies, and time information when available	Relative timestamps can be approximate
Stores results in an Apify dataset	Current sort labels do not exactly match runtime checks

Comparison With Alternative Approaches

Capability	This Actor	Manual / Typical Alternative
Process multiple selected Shorts	Supported through startUrls	Often repeated manually
Structured comment records	Returned as structured output	May require manual formatting
Comment limit	Supported with maxComments	Depends on the collection method
Request all comments	Supported with getAllComments	Depends on the collection method
Keep comments linked to videos	Included with video IDs and URLs	May require manual organization
Apify dataset output	Supported	Often needs an additional collection step

Best Practices

Use direct YouTube Shorts URLs and begin with a small test run.

Use a positive maxComments value when you want predictable collection volume. Use getAllComments: true when you need a broader comment set.

Keep videoId and videoUrl in downstream datasets so comments remain traceable to their source.

Expect optional values to vary. Check for missing or null fields before using title, author, comment ID, or timestamps in later processing.

For research or business analysis, validate important results before treating estimated timestamps or scraped totals as final source-of-record values.

Troubleshooting

Invalid input: Check that startUrls is an array containing one or more objects with a url value.

Empty results: Verify the supplied video URL and whether comments are available for the selected video. Not every metadata field is guaranteed to be present.

Fewer comments than expected: Check maxComments and getAllComments. With all-comments mode disabled, collection stops when the maximum is reached.

Missing title or comment count: These values are obtained on a best-effort basis. The title can be null, and commentsCount can use the scraped count as a fallback.

Missing timestamp: publishedTimeTs depends on recognizable relative-time text. It can be null when the text cannot be parsed.

Unexpected ordering: The current UI values are top comments and newest comments, but the runtime checks top and newest. This mismatch can prevent the intended sort behavior.

Frequently Asked Questions

What does the Youtube Shorts Comments Scraper do? It extracts comments from YouTube Shorts or other supported YouTube video URLs provided in startUrls and returns structured video and comment data.

Can I process multiple Shorts in one run? Yes. startUrls accepts an array, so multiple URLs can be submitted together.

Can I limit the number of comments? Yes. Set maxComments to a positive integer when getAllComments is disabled.

How do I request all available comments? Set getAllComments to true. The scraping loop then continues without stopping at the maximum comment setting.

What information is included for each comment? A record can include comment ID, text, author, published-time text, estimated timestamp, reply count, vote count, video ID, and source URL.

Does the Youtube Shorts Comments Scraper return the video title? It attempts to return the title, but the field can be null when the title cannot be obtained.

Is commentsCount always the exact total number of platform comments? No. When the page-level count cannot be determined, the Actor falls back to the number of comments actually scraped.

Can I use publishedTimeTs as an exact timestamp? No. It is an estimated Unix timestamp derived from relative-time text and may be approximate.

Why might the selected sort option not change the order? The exposed values are top comments and newest comments, while the runtime checks for top and newest. The mismatch can prevent the sort branch from being applied.

Is this Actor suitable for automated research workflows? Yes, the documented workflow is based on direct URL input and structured Apify dataset output, making it suitable for repeatable collection tasks.

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Final Overview

The Youtube Shorts Comments Scraper is designed for users who need structured comment data from selected YouTube Shorts URLs. It supports multiple video inputs, configurable comment collection, all-comments mode, and dataset-ready output that keeps comments connected to their source videos.

The core workflow is simple: provide URLs, configure collection, run the Actor, and review the resulting dataset. For downstream analysis, account for optional fields, estimated timestamps, and the current sort-value mismatch described in the limitations section.

Contact me: Alphascraper69@gmail.com