Facebook Page Recommendations Scraper

Extract anonymous public recommendation records from Facebook Page Reviews tabs.

Provide one or more public Page URLs and receive normalized records containing the reviewer, recommendation decision, text, displayed time, public post URL, engagement counts when visible, and Page-level reputation summary.

The Actor is designed for recurring reputation monitoring. It does not ask for Facebook credentials, cookies, or private account access.

What does Facebook Page Recommendations Scraper do?

The Actor opens each supplied public Facebook Page in Chromium and reads recommendation cards that Facebook exposes to logged-out visitors.

It:

• normalizes Page and /reviews URLs;
• extracts individual public recommendation records;
• attaches the Page recommendation percentage and review count to every record;
• follows public recommendation post permalinks when Facebook exposes them;
• removes duplicate records by permalink or a stable content hash;
• stops at your global maxItems limit;
• fails clearly when no useful public records are visible.

It does not scrape private profiles, authenticate as a user, or accept account cookies.

Who is it for?

Reputation-management agencies

Track fresh customer experiences across client Pages and load normalized records into reporting pipelines.

Multi-location and franchise brands

Snapshot Page reputation metrics and recommendation text for selected brand or location Pages.

Social-listening teams

Combine public Facebook recommendations with review feeds from other channels for triage or sentiment analysis.

Researchers

Collect reproducible public observations with source URLs and scrape timestamps.

Why use this Actor?

• Public-only workflow: no private Facebook session is required or accepted.
• Record-level output: this is not only a Page metadata scraper.
• Page context included: each row carries the recommendation percentage and Page review count.
• Explicit failures: login-only, unavailable, challenged, and zero-public-record pages do not silently become successful empty datasets.
• Bounded browser work: scrolling and result volume have strict limits.
• Pipeline-ready: use JSON, CSV, Excel, API, webhooks, Make, Zapier, or MCP.

What data can you extract?

Field	Meaning
reviewId	Facebook post ID when visible, otherwise a stable record hash
reviewUrl	Public recommendation post permalink, or null
reviewerName	Public display name
reviewerProfileUrl	Public profile URL when Facebook exposes one, otherwise null
recommends	true for recommends and false for does not recommend
text	Recommendation text
timestampText	Timestamp exactly as displayed, such as 1h
publishedAt	Absolute time when available; currently null for relative-only public cards
reactionsCount	Visible reaction count, or null
commentsCount	Visible comment count, or null
pageName	Facebook Page name
pageUrl	Canonical Page URL
reviewsUrl	Canonical Reviews tab URL
recommendationPercent	Visible Page recommendation percentage, or null
reviewCount	Visible Page review count, or null
followersText	Public follower summary, or null
sourceUrl	Normalized input URL processed for this record
scrapedAt	UTC collection timestamp

How to scrape Facebook Page recommendations

1. Open the Actor on Apify.
2. Add one or more canonical public Facebook Page URLs.
3. Choose the maximum number of recommendations.
4. Keep the default scroll settings for a normal snapshot.
5. Click Start.
6. Open the default dataset to download JSON, CSV, Excel, XML, or RSS.

A simple input is:

{
  "startUrls": [
    { "url": "https://www.facebook.com/McDonalds/reviews" }
  ],
  "maxItems": 20,
  "maxScrolls": 4,
  "scrollWaitMillis": 1500
}

Input parameters

startUrls

Required array of public canonical Facebook Page URLs. URLs may end at the Page slug or include /reviews; the Actor normalizes both forms.

Supported:

https://www.facebook.com/McDonalds
https://www.facebook.com/McDonalds/reviews

Private URLs, profile.php identifiers, non-Facebook hosts, and non-HTTPS URLs are rejected.

maxItems

Maximum records across all Pages. Allowed range: 1–500. Default: 20.

maxScrolls

Maximum bounded scroll attempts per Page. Allowed range: 0–20. Default: 4. Use 0 for only initially rendered records.

scrollWaitMillis

Wait after a scroll so newly exposed public cards can render. Allowed range: 500–5,000 milliseconds. Default: 1,500.

Output example

This abbreviated record reflects current public output from the McDonald's Page:

{
  "reviewId": "pfbid07FWg4sbEUozz8zWukPsvzJ2wNeTBxVj2rEn94UcnMugtbrbpJNGVtPAr4cHbAR7fl",
  "reviewUrl": "https://www.facebook.com/alii.aliss.7/posts/pfbid07FWg4sbEUozz8zWukPsvzJ2wNeTBxVj2rEn94UcnMugtbrbpJNGVtPAr4cHbAR7fl",
  "reviewerName": "Alexandra Ionescu",
  "reviewerProfileUrl": null,
  "recommends": false,
  "text": "Personal necalificat care nu își respectă clienții !\nTerrible delivery service",
  "timestampText": "1h",
  "publishedAt": null,
  "reactionsCount": null,
  "commentsCount": null,
  "pageName": "McDonald's",
  "pageUrl": "https://www.facebook.com/McDonalds",
  "reviewsUrl": "https://www.facebook.com/McDonalds/reviews",
  "recommendationPercent": 32,
  "reviewCount": 37834,
  "followersText": "82M followers",
  "sourceUrl": "https://www.facebook.com/McDonalds/reviews",
  "scrapedAt": "2026-07-27T04:40:45.109Z"
}

Relative timestamps and counts change on Facebook. Treat this as a field-shape example, not a promise that the same recommendation remains first.

How much does it cost to scrape Facebook Page recommendations?

Pay per event pricing has two parts on the FREE tier:

• $0.005 per Actor start;
• $0.000041071 per successfully emitted recommendation.

There is no recommendation charge for a Page that produces no dataset record. Platform usage limits still apply. Higher Apify pricing tiers receive lower per-recommendation prices.

Examples at FREE-tier event prices:

Useful output	Event price
1 recommendation	$0.005041071
10 recommendations	$0.00541071
100 recommendations	$0.00910710

Actual availability and runtime vary because Facebook controls anonymous delivery.

Monitoring workflow examples

Daily reputation snapshot

Schedule the Actor daily, set a webhook on successful runs, and upsert rows by reviewId in your warehouse.

Multi-location review triage

Supply several location Pages, filter new rows where recommends is false, and route them to a customer-experience queue.

Cross-channel analysis

Export recommendation text and Page summary fields to BigQuery, Snowflake, Google Sheets, or a sentiment-classification workflow.

Tips for reliable results

• Use canonical Page slugs rather than shared or shortened links.
• Start with a small maxItems while validating a Page.
• Keep maxScrolls conservative; logged-out Facebook often exposes only a small public sample.
• Schedule snapshots rather than expecting a complete historical archive from one anonymous visit.
• Deduplicate downstream by reviewId.
• Preserve scrapedAt and sourceUrl for auditability.

Limitations and failure behavior

Facebook decides which recommendation cards are available to logged-out visitors. Availability varies by Page, country, time, and Facebook experiments.

The Actor does not promise every historical review. It only emits records visible on the public anonymous surface during the run.

A run fails rather than returning a misleading empty success when all requested Pages are unavailable, challenged, login-only, or expose zero public recommendation records.

If at least one Page succeeds, valid rows are preserved and individual Page failures are logged as warnings.

Images, videos, and fonts are blocked because they are unnecessary for textual recommendation extraction.

Use the Apify API with cURL

curl -X POST \
  "https://api.apify.com/v2/acts/automation-lab~facebook-page-recommendations-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.facebook.com/McDonalds/reviews"}],"maxItems":10}'

To wait and retrieve dataset items:

curl -X POST \
  "https://api.apify.com/v2/acts/automation-lab~facebook-page-recommendations-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.facebook.com/McDonalds/reviews"}],"maxItems":10}'

Use the Apify API with JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('automation-lab/facebook-page-recommendations-scraper').call({
  startUrls: [{ url: 'https://www.facebook.com/McDonalds/reviews' }],
  maxItems: 10,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Use the Apify API with Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("automation-lab/facebook-page-recommendations-scraper").call(run_input={
    "startUrls": [{"url": "https://www.facebook.com/McDonalds/reviews"}],
    "maxItems": 10,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

Use Facebook recommendations through MCP

Add the Actor to Claude Code:

claude mcp add --transport http apify \
  "https://mcp.apify.com?tools=automation-lab/facebook-page-recommendations-scraper"

Use the same HTTP server configuration in Claude Desktop, Cursor, or VS Code MCP settings:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=automation-lab/facebook-page-recommendations-scraper"
    }
  }
}

Example prompts:

• “Collect up to 20 public recommendations from this Facebook Page and summarize recurring complaints.”
• “Compare public recommendation percentages across these three brand Pages.”
• “Return only new negative recommendations not present in this list of review IDs.”

Integrations

Use Apify webhooks to start downstream processing when a run succeeds. The default dataset works with Make, Zapier, Google Sheets, Slack, cloud storage, and database integrations.

For recurring monitoring, schedule runs in Apify Console and retain reviewId in the destination system for incremental processing.

Responsible use and legality

Scrape only public information and follow applicable laws, contracts, and Facebook terms. Do not use the output for harassment, discrimination, identity profiling, or attempts to access private accounts.

Reviewer names and profile links can be personal data. Establish a lawful purpose, collect only what you need, secure the output, honor deletion obligations, and apply appropriate retention limits.

This Actor is an independent automation tool and is not affiliated with or endorsed by Meta or Facebook.

Troubleshooting

Why did the run say no public recommendation records were found?

The Page may not have a public Reviews tab, Facebook may be showing a login-only surface, the Page may be unavailable, or anonymous visibility may have changed. Confirm the canonical Page URL in a logged-out browser and retry later if the condition is temporary.

Why did I receive fewer rows than maxItems?

maxItems is a ceiling, not a guarantee. Logged-out Facebook frequently exposes a bounded public sample, and scrolling does not guarantee more records.

Why are engagement counts or profile URLs null?

Facebook does not show every field on every anonymous card. Nullable fields remain null rather than being guessed.

Can I provide my Facebook login or cookies?

No. The Actor intentionally supports public anonymous data only and has no credential input.

FAQ

Does the Actor scrape Facebook star ratings?

Facebook Page recommendations are generally expressed as recommends/does not recommend plus a Page recommendation percentage. The Actor reports those public fields; it does not invent star ratings.

Can I scrape multiple Pages?

Yes. Add up to 50 Page URLs. maxItems applies across the complete run.

Does it return old recommendations?

Only when Facebook exposes them to the anonymous rendered surface during the run. Use scheduled snapshots for ongoing monitoring.

Is each Page summary a separate charged row?

No. Page summary fields are attached to each recommendation. Only emitted recommendation records have the recommendation event charge.

Related automation-lab Actors

For broader public Facebook Page metadata rather than individual recommendation rows, use Facebook Pages Scraper.

Combine Page recommendations with other review-channel Actors in the automation-lab portfolio when your workflow needs cross-source reputation monitoring.