How to Get Facebook Comments From Public Posts

Public Facebook comments can provide useful feedback about products, campaigns, events, competitors, and customer concerns. However, manually opening every reply thread and copying comments into a spreadsheet quickly becomes impractical.

The simplest workflow is to collect the relevant public post URLs, process them with a comment-extraction tool, review the structured results, and export only the fields needed for analysis. A ready-made platform such as CoreClaw lets teams complete this process without building or maintaining a scraper.

What Facebook Comment Data Can You Collect?

A Facebook comment dataset may include:

Facebook states that comments on Page posts and public-group posts are public. However, visibility can still vary by account settings, deletion status, location, and how Facebook loads or ranks a discussion.

Three Ways to Get Comments From Facebook Posts

1. Review or copy the comments manually

Manual collection may be enough when a post has only a few comments. Open the post, change the sorting option when necessary, expand the reply threads, and copy the relevant text into a spreadsheet.

This method becomes slow and error-prone once a post contains hundreds of comments. It also makes it difficult to preserve IDs, timestamps, source links, and parent-reply relationships.

2. Use Meta’s official Graph API

The official Graph API is generally the appropriate route for comments on Pages that an organization owns or manages. Access depends on the Page, access token, application permissions, API version, and tasks granted to the requesting user.

Meta’s current Page Post Comments documentation states that accessing certain comment fields requires the ability to perform the MODERATE task on the Page. The API should therefore not be treated as unrestricted access to comments from every public post.

3. Use a public-post comment scraper

A comment scraper accepts one or more supported Facebook post URLs and returns structured records. This is usually the most practical option when the research involves selected public posts that the organization does not manage.

The CoreClaw Facebook Comments Scraper collects public comment content, commenter fields, interaction metrics, timestamps, reply counts, and nested reply lists. It supports batch post URLs and no-code CSV or JSON export.

How to Get Facebook Comments with CoreClaw

Step 1: Collect the Public Post URLs

Open each target post and copy its direct URL. The source may be a public Page post, video, Reel, or another supported public Facebook post type.

Place one URL per spreadsheet row. Add optional columns for campaign, competitor, market, topic, or collection date. Remove duplicate URLs before starting the task.

Step 2: Configure the Comment Task

Open the Facebook Comments Scraper and add the post URLs. Set a result limit based on the project instead of automatically requesting every available record.

Decide whether replies are required. Top-level comments may be sufficient for a quick campaign review, while product-feedback analysis often benefits from the full conversation thread.

The Worker also supports reply-depth controls and comment sorting options.

Step 3: Run a Small Test

Start with one or two posts before processing the complete list.

Review whether the output contains the expected comment text, dates, commenter fields, likes, IDs, and replies. Compare several exported records with the original Facebook discussion.

A smaller result count than Facebook’s displayed total does not necessarily indicate a failed task. The displayed number may include inaccessible comments, private activity, deleted content, and replies counted within the total.

Step 4: Review and Export the Results

After validating the test, process the remaining URLs. CoreClaw displays the results as structured rows and supports CSV or JSON downloads directly from the Worker.

Developers can use the CoreClaw API integration to start a Worker, monitor its run, retrieve results, and connect the data to dashboards or internal applications. Export endpoints support CSV, JSON, JSONL, XLSX, XLS, XML, HTML, and RSS, with optional field filtering.

How to Clean Facebook Comment Data

A completed extraction is not automatically an analysis-ready dataset.

Remove duplicates using the comment ID rather than comment text alone. Keep the parent-comment field so replies remain connected to their original threads. Standardize timestamps, URLs, reaction values, empty fields, and language labels.

For customer-feedback analysis, add classification columns such as:

• Topic
• Sentiment
• Product or feature
• Question
• Complaint
• Purchase intent
• Requires manual review

Exclude spam, giveaway entries, repeated emoji-only comments, and irrelevant conversations when they do not support the research goal.

Automated sentiment classification should not be treated as completely accurate. Sarcasm, slang, mixed languages, and missing context still require sample-based human review.

Limits and Responsible-Use Practices

Only collect data that is necessary for a documented purpose. Do not target private groups, friends-only posts, direct messages, login-restricted content, or information protected by access controls.

Keep the post URL, comment URL, collection timestamp, and project purpose with the dataset. Establish a retention period and restrict access when comments contain personal information.

Meta’s terms state that automated collection generally requires prior permission, and its Automated Data Collection Terms govern authorized collection arrangements. Public visibility does not remove every contractual, privacy, copyright, or data-protection obligation.

Important findings should always be checked against a sample of the original discussions before they are used for business decisions.

Conclusion

Getting Facebook comments from public posts is easiest when the workflow begins with known URLs and ends with a focused, verified dataset. Manual copying works for very small discussions, while Meta’s official API is better suited to authorized Page-management use cases.

With CoreClaw, teams can process public post URLs through a ready-made Worker, collect comments and nested replies without coding, clean and filter the results, and export them to CSV, JSON, or Excel-compatible formats. Recurring projects can use the API, while specialized requirements can be handled through a custom Worker request.

CoreClaw follows a pay-only-for-successful-results pricing model, and developers can also publish reusable scraping Workers to the Store.

For a vendor comparison, read 5 Best Facebook Comments Scraper Tools.