Facebook Page Scraper: Extract Posts, Comments, and Page Details

A Facebook Page scraper collects accessible information from public Facebook Pages and converts it into structured records. Depending on the workflow, teams may collect Page names, descriptions, categories, public websites, post text, publishing dates, media links, reactions, shares, comments, and reply threads.

The value comes from connecting these records. Page details identify the source, posts show what the Page publishes, and comments reveal visible audience responses. With a cloud-based web scraping platform, teams can replace repetitive page review with cleaner datasets that are easier to filter, export, and analyze.

What Is a Facebook Page Scraper?

A Facebook Page scraper is a tool that reads accessible Page or post URLs and organizes selected fields into tables or machine-readable formats. A no-code scraper lets business users enter URLs and run a ready-made workflow without writing Python or maintaining browser infrastructure.

The CoreClaw Facebook Scraper Store contains Workers for public Page and profile details, posts, comments, events, ads, and other supported Facebook sources. Results are returned as structured fields rather than an unorganized copy of the webpage.

A scraper is different from Meta’s official Graph API. The API is designed for approved applications and authorized platform use cases, while a scraper processes accessible web content. The appropriate option depends on Page ownership, permissions, target fields, and the intended use.

What Public Facebook Page Data Can You Extract?

The available fields depend on public visibility, the input URL, the selected Worker, and Facebook’s current interface.

Page Details

Page-level records establish who published the content. Useful fields may include:

• Page name and URL
• Description or biography
• Category
• Public website
• Profile and cover image
• Follower- or like-related information
• Page identifiers
• Collection timestamp

Not every Page exposes the same fields. Some Pages provide a detailed business description and external website, while others display only basic identity information.

Posts and Engagement Metrics

Post records help teams compare publishing activity, content topics, formats, and visible engagement.

The Facebook Posts Scraper can process public Facebook Pages, profiles, groups, or post URLs. Its documented output includes post text, links, author information, publishing dates, hashtags, images, reactions, comment counts, like counts, and share counts.

A useful post dataset should preserve both the Page URL and post URL. These fields connect every post to its source and make later validation possible.

Comments and Replies

Comment data adds qualitative context that reaction totals cannot provide. It can reveal common questions, product complaints, campaign reactions, requests, objections, and the language audiences use when discussing a topic.

The Facebook Comments Scraper accepts one or more public post URLs and returns structured comment content, commenter fields, interaction metrics, timestamps, reply counts, and available reply lists. It supports no-code runs and CSV or JSON output.

How to Scrape Facebook Page Data with CoreClaw

1. Define the Target Pages

Begin with a specific research question. For example:

• Which topics do competing Pages publish most frequently?
• Which product posts receive the most visible engagement?
• What questions appear repeatedly under campaign posts?
• How does audience response differ across selected brands?

Create a source list containing the Page name, Page URL, category, and reason for inclusion. This reduces unrelated results and makes the project easier to audit.

2. Collect Page Posts

Open the Facebook Posts Scraper, add the relevant Page or post URLs, and run a small test.

Review the first records before increasing the collection size. Confirm that post text, timestamps, source URLs, media links, and engagement values are stored consistently. A short test is also useful for estimating the number of relevant results.

3. Select Posts for Comment Extraction

Comments should normally be collected after the post dataset has been filtered. Select posts according to the project’s date range, topic, campaign, product, or visible engagement.

Pass only those post URLs to the Facebook Comments Scraper. This two-stage workflow produces a more focused dataset than collecting every available comment from every Page post.

4. Clean and Export the Results

Remove duplicate URLs, empty records, irrelevant content, unavailable posts, and fields that do not support the research question. Standardize Page names, timestamps, IDs, and engagement values.

CoreClaw supports CSV, JSON, JSONL, XLSX, XLS, XML, HTML, and RSS exports. Spreadsheet formats are useful for manual review and reporting, while JSON works better for applications and data pipelines. Exported fields can also be limited through the API when a workflow needs only selected columns.

Recurring projects can use the CoreClaw API integration to manage task templates and schedules instead of configuring every run manually.

How to Structure a Facebook Page Dataset

A practical dataset can use three connected tables:

Page IDs connect posts to their publishers, while post IDs connect comments to the correct content. This structure is easier to analyze than placing every field into one oversized spreadsheet.

Keep a collection timestamp and original source URL in each table. Facebook content and visible engagement can change after extraction.

Practical Use Cases

Competitor monitoring: Compare publishing frequency, formats, topics, hashtags, and engagement across selected Pages.

Campaign analysis: Collect campaign posts and review comments for questions, objections, praise, or recurring problems.

Brand research: Organize public conversations about products, services, customer experiences, and market expectations.

Content planning: Identify frequently discussed subjects and high-engagement formats without opening every Page manually.

AI-assisted research: Prepare cleaned and filtered records for classification, summarization, topic grouping, or sentiment review. Automated labels should still be sampled because sarcasm, emojis, jokes, and missing context can affect interpretation.

Applicable Workers use pay-only-for-successful-results pricing, although rates vary by Worker and plan. Failed result rows are not charged as successfully delivered entries under this model.

Limitations and Responsible Collection

Public visibility does not mean that every collection or reuse method is permitted. Teams should review Meta’s current terms, applicable privacy laws, contractual requirements, and the intended use of the dataset before starting a project.

Avoid private content, restricted Pages, access-control bypasses, sensitive attributes, unnecessary personal data, and information involving minors. Collect only the fields needed for the documented research purpose and apply appropriate retention and access controls.

Results may be incomplete because posts can be deleted, comments can be hidden, engagement counts change, and Facebook may alter its page layouts. Important findings should be validated against a sample of the original Pages.

Conclusion

A Facebook Page scraper is most useful when Page context, post content, and comments are treated as connected data layers. This structure helps teams move from a broad source list to focused content analysis and more useful audience research.

With CoreClaw, teams can run ready-made Facebook Workers without coding, work with cleaned and filtered structured outputs, export results to spreadsheet or developer formats, and automate recurring tasks through an API. When the existing Store does not match a specialized schema or target source, teams can request a custom Worker. Developers can also publish and monetize scraping Workers after platform review.