Facebook Reviews & Recommendations Scraper

Scrape publicly visible Facebook Page reviews and recommendations, normalize review text, reviewer signals, timestamps and recommendation context, and return structured rows for reputation monitoring and customer research.

Why use this Actor

Teams need current structured source data without repeatedly rebuilding brittle collection workflows.

Key features

• Buyer-native inputs aligned to Facebook reviews scraper.
• Predictable structured output in the default Apify Dataset.
• Pay Per Event pricing tied to measurable result rows or reports.
• Optional MCP connector delivery for downstream automation and AI agents.

Quick start

Start with this working example and replace the target values with your own:

{
  "pageUrls": [
    "https://www.facebook.com/TajWestEnd"
  ],
  "maxReviews": 20
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• pageUrls — Facebook Page URLs: Public Facebook Page URLs whose Reviews or Recommendations surface should be collected.
• maxReviews — Maximum reviews: Maximum publicly visible review or recommendation rows returned.
• diagnostic — Diagnostic mode: Return bounded browser diagnostics only if no review rows can be normalized.
• urls — URLs: Alias for Facebook Page URLs. Use pageUrls for the clearest buyer-facing input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.003500 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.