Facebook Comments Scraper

Export public Facebook post comments into a structured dataset for social listening, creator intelligence, moderation review, campaign reporting, and brand monitoring.

Use this Actor when you need a repeatable way to collect visible public comments from one or more Facebook post URLs, download them as CSV/JSON/Excel, or call them from a workflow as a Facebook comments API alternative.

At a glance

• Primary job: Export Facebook comments data from public post URLs.
• Input: One or more public Facebook post URLs plus a per-post comment limit.
• Output: One dataset row per saved comment with author name, comment text, relative date, likes, source post URL, raw visible text, and scrape timestamp.
• Best for: Social listening, engagement analysis, moderation triage, creator intelligence, dashboards, alerts, and research exports.

Who is it for?

• Social listening teams tracking public reactions to brand, creator, media, or campaign posts.
• Moderation and community teams reviewing public comment text, authors, and source URLs in downstream tools.
• Analysts and researchers building repeatable Facebook comments exports for dashboards, notebooks, alerts, or archives.

Common workflows

• Brand monitoring: Schedule repeat runs for campaign or announcement posts and compare newly visible comments over time.
• Creator intelligence: Export audience replies from public creator posts for topic, sentiment, or engagement analysis.
• Moderation review: Send comment text, author names, and source URLs to review queues or BI tools.
• Automation: Use the Apify API, webhooks, MCP, or dataset exports to feed alerts, warehouses, notebooks, and reporting workflows.

What data can you extract?

Example input

{
  "startUrls": [
    {
      "url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl"
    }
  ],
  "maxCommentsPerPost": 20
}

Example output

{
  "commentId": "a1b2c3d4e5f6",
  "sourcePostUrl": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl",
  "postTitle": "Humans of New York",
  "authorName": "Jane Doe",
  "authorUrl": null,
  "commentText": "This story is beautiful.",
  "relativeDate": "2w",
  "likeCount": 12,
  "replyDiagnostics": "1 reply",
  "commentPermalink": null,
  "rawText": "Jane Doe 2w This story is beautiful. 12 1 reply",
  "scrapedAt": "2026-07-31T18:42:04.000Z"
}

Tips for best results

• Use public Facebook post URLs that load without being logged in.
• Start with a low maxCommentsPerPost value, review the dataset, then scale scheduled runs.
• Split very large monitoring jobs into smaller batches so one blocked or private post does not delay unrelated posts.
• Keep source post URLs in your downstream database so repeat exports can be deduplicated by commentId and sourcePostUrl.

API usage

Node.js:

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("fetch_cat/facebook-comments-scraper").call({
  startUrls: [
    { url: "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl" }
  ],
  maxCommentsPerPost: 20
});
console.log(run.defaultDatasetId);

Python:

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("fetch_cat/facebook-comments-scraper").call(run_input={
    "startUrls": [
        {"url": "https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl"}
    ],
    "maxCommentsPerPost": 20,
})
print(run["defaultDatasetId"])

cURL:

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~facebook-comments-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.facebook.com/humansofnewyork/posts/pfbid0BbKbkisExKGSKuhee9a7i86RwRuMKFC8NSkKStB7CsM3uXJuAAfZLrkcJMXxhH4Yl"}],"maxCommentsPerPost":20}'

MCP and AI agents

Use this Actor from MCP-compatible tools through the official Apify MCP Server.

claude mcp add --transport http apify "https://mcp.apify.com?tools=fetch_cat/facebook-comments-scraper"

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/facebook-comments-scraper"
    }
  }
}

Example prompt: "Run Facebook Comments Scraper for this public post URL and summarize the most common customer complaints in the exported comments."

Limits and caveats

• The Actor runs against public, visible Facebook post pages. It does not log in, bypass privacy settings, or return private comments.
• Facebook can hide, reorder, or limit comments in public views; the dataset reflects what was visible during the run.
• Some optional fields, especially author URLs and comment permalinks, are only saved when Facebook exposes them publicly.
• Relative dates are captured as displayed by Facebook. Use scrapedAt with relativeDate for freshness analysis.
• Process only public data you are allowed to access, and follow applicable terms and laws.

Support

Open an issue from the Actor page if a run fails or output looks wrong. Include the run ID or run URL, input JSON, expected output, actual output, and one reproducible public Facebook post URL.