Fake Review Checker: Incentivized & Duplicates

Screen Amazon, Trustpilot, Google Play, App Store, Tripadvisor or any scraped reviews for the warning signs the FTC's 2024 rule on fake reviews (16 CFR 465) targets. The Fake Review Checker gives each review a 0-100 signal score based on 5-star-for-reward wording, free-product or paid reviews, reviews by owners, staff or relatives, copied text across reviews, 5-star bursts on one day, 5-star ratings with negative text and reviews written before using the product. Each signal shows the matched words, its weight and the rule or guide it comes from. Click Start with the prefilled example to see it work.

This is an automated screen for warning signs, not a finding that any review is fake or that anyone broke a law or platform rule. A zero score doesn't mean a review is genuine. Not legal advice; every row carries this disclaimer. Reviewer IDs are only used for matching and are never output.

What does the Fake Review Checker do?

• ✅ Reward for a positive review (40 points): "in exchange for a 5-star review", "if I leave 5 stars" and similar. The FTC rule bans conditioning a reward on the review's sentiment.
• ✅ Insider reviews (30): "my brother owns the company", "I work for this brand", "our product".
• ✅ Copied text (30): near-identical reviews (12+ words) for the same product in your data.
• ✅ Disclosed incentives (15): "received a free sample", "#gifted", "#sponsored", "discounted in exchange for my honest review". Allowed when disclosed, but worth tracking. Vine and other platform programs score 3.
• ✅ Review suppression (15): text describing reviews being removed, blocked or paid to be changed.
• ✅ Review bursts (15): a day with many reviews (default 5+), at least 4x the product's median, mostly 4.5+ stars.
• ✅ Rating and text mismatch (10) and no experience yet (10): 5 stars with "terrible, broke after two days", or "haven't tried it yet but...".
• ✅ Output per review: integrityRiskScore (0-100), signalLevel, signalCount, categoriesFlagged, signals (signalId, category, weight, quote, context, sourceUrl, whyItMatters), rating, reviewDate, wordCount, checkedAt and a disclaimer.

Who is it for?

• Brands and marketplace sellers auditing their own review pages before the FTC or a platform does.
• Agencies and review-management firms checking that client review programs don't use sentiment-conditioned rewards.
• Shoppers, researchers and journalists measuring incentivized or copied reviews in scraped data.
• Marketplaces and review platforms triaging reviews for moderation.

How to check reviews for fake review signs

1. Click Start with the prefilled example (seven blender reviews with several signals and two ordinary reviews).
2. Replace it with your reviews: text, and optionally rating, title, date, productId, reviewer and id. Or paste plain texts into Texts only.
3. Or enter a Dataset ID from an Amazon, Trustpilot, Google Play or other review scraper. Common fields are found automatically; use Field map (dotted paths work) for others.
4. Include date and productId to enable the burst check, and rating for the mismatch check.
5. Turn on Only save rows with findings for big runs; you only pay for saved rows.

Input example

This is the prefilled input:

{
  "reviews": [
    {
      "id": "R1",
      "productId": "EXAMPLE-BLENDER",
      "rating": 5,
      "date": "2026-10-01",
      "reviewer": "a1",
      "text": "I received this blender for free in exchange for a 5-star review. Works great!"
    },
    {
      "id": "R2",
      "productId": "EXAMPLE-BLENDER",
      "rating": 5,
      "date": "2026-10-02",
      "reviewer": "a2",
      "text": "My brother owns the company, but honestly this is the best blender I've ever used."
    },
    {
      "id": "R3",
      "productId": "EXAMPLE-BLENDER",
      "rating": 5,
      "date": "2026-10-03",
      "reviewer": "a3",
      "text": "This blender is powerful and quiet, crushes ice in seconds and cleans up easily in the dishwasher. Highly recommend it to anyone."
    },
    {
      "id": "R4",
      "productId": "EXAMPLE-BLENDER",
      "rating": 5,
      "date": "2026-10-03",
      "reviewer": "a4",
      "text": "This blender is powerful and quiet, crushes ice in seconds and cleans up easily in the dishwasher. Highly recommend it to everyone."
    },
    {
      "id": "R5",
      "productId": "EXAMPLE-BLENDER",
      "rating": 5,
      "date": "2026-10-04",
      "reviewer": "a5",
      "text": "Terrible. Broke after two days and the lid leaks."
    },
    {
      "id": "R6",
      "productId": "EXAMPLE-BLENDER",
      "rating": 4,
      "date": "2026-10-05",
      "reviewer": "a6",
      "text": "Solid blender for smoothies. A bit loud on high, but the jar is sturdy and easy to clean."
    },
    {
      "id": "R7",
      "productId": "EXAMPLE-BLENDER",
      "rating": 2,
      "date": "2026-10-06",
      "reviewer": "a7",
      "text": "The motor smells hot after a minute. Returning it."
    }
  ]
}

Dataset mode:

{"datasetId": "YOUR_DATASET_ID", "fieldMap": {"text": "reviewDescription", "rating": "ratingScore", "date": "reviewDate", "productId": "asin"}, "onlyFlagged": true, "maxItems": 50000}

Output example

Real output from a run of the prefilled input on 2026-10-30 (long finding lists are shortened here; the run returns all of them):

[
  {
    "itemNo": 1,
    "reviewId": "R1",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 40,
    "signalLevel": "strong signals",
    "signalCount": 1,
    "categoriesFlagged": [
      "incentive"
    ],
    "signals": [
      {
        "signalId": "sentiment_conditioned_incentive",
        "category": "incentive",
        "weight": 40,
        "quote": "in exchange for a 5-star review",
        "context": "I received this blender for free in exchange for a 5-star review. Works great!",
        "sourceUrl": "https://www.ecfr.gov/current/title-16/chapter-I/subchapter-D/part-465",
        "whyItMatters": "Compensation offered for a positive (or negative) review is prohibited by 16 CFR 465.4."
      }
    ],
    "rating": 5,
    "reviewDate": "2026-10-01",
    "wordCount": 15,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  },
  {
    "itemNo": 2,
    "reviewId": "R2",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 30,
    "signalLevel": "some signals",
    "signalCount": 1,
    "categoriesFlagged": [
      "insider"
    ],
    "signals": [
      {
        "signalId": "insider_connection",
        "category": "insider",
        "weight": 30,
        "quote": "My brother owns the company",
        "context": "My brother owns the company, but honestly this is the best blender I've ever ...",
        "sourceUrl": "https://www.ecfr.gov/current/title-16/chapter-I/subchapter-D/part-465",
        "whyItMatters": "Reviews by a company's officers, managers, employees or agents without a clear disclosure are restricted by 16 CFR 465.5."
      }
    ],
    "rating": 5,
    "reviewDate": "2026-10-02",
    "wordCount": 15,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  },
  {
    "itemNo": 3,
    "reviewId": "R3",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 30,
    "signalLevel": "some signals",
    "signalCount": 1,
    "categoriesFlagged": [
      "duplication"
    ],
    "signals": [
      {
        "signalId": "duplicate_text",
        "category": "duplication",
        "weight": 30,
        "quote": "This blender is powerful and quiet, crushes ice in seconds and cleans up easily ",
        "context": null,
        "sourceUrl": "https://www.ecfr.gov/current/title-16/chapter-I/subchapter-D/part-465",
        "whyItMatters": "The same or nearly the same text appears in 1 other review(s) in this run (rows 4); copied reviews are a common sign of fake reviews.",
        "matchedRows": [
          4
        ]
      }
    ],
    "rating": 5,
    "reviewDate": "2026-10-03",
    "wordCount": 22,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  },
  {
    "itemNo": 4,
    "reviewId": "R4",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 30,
    "signalLevel": "some signals",
    "signalCount": 1,
    "categoriesFlagged": [
      "duplication"
    ],
    "signals": [
      {
        "signalId": "duplicate_text",
        "category": "duplication",
        "weight": 30,
        "quote": "This blender is powerful and quiet, crushes ice in seconds and cleans up easily ",
        "context": null,
        "sourceUrl": "https://www.ecfr.gov/current/title-16/chapter-I/subchapter-D/part-465",
        "whyItMatters": "The same or nearly the same text appears in 1 other review(s) in this run (rows 3); copied reviews are a common sign of fake reviews.",
        "matchedRows": [
          3
        ]
      }
    ],
    "rating": 5,
    "reviewDate": "2026-10-03",
    "wordCount": 22,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  },
  {
    "itemNo": 5,
    "reviewId": "R5",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 10,
    "signalLevel": "weak signals",
    "signalCount": 1,
    "categoriesFlagged": [
      "consistency"
    ],
    "signals": [
      {
        "signalId": "rating_text_mismatch",
        "category": "consistency",
        "weight": 10,
        "quote": "Terrible",
        "context": "Terrible. Broke after two days and the lid leaks.",
        "sourceUrl": "https://www.ecfr.gov/current/title-16/chapter-I/subchapter-D/part-465",
        "whyItMatters": "A top rating with clearly negative text can mean a misclick, or a review whose rating doesn't reflect the writer's experience."
      }
    ],
    "rating": 5,
    "reviewDate": "2026-10-04",
    "wordCount": 9,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  },
  {
    "itemNo": 6,
    "reviewId": "R6",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 0,
    "signalLevel": "no listed signals found",
    "signalCount": 0,
    "categoriesFlagged": [],
    "signals": [],
    "rating": 4,
    "reviewDate": "2026-10-05",
    "wordCount": 18,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  },
  {
    "itemNo": 7,
    "reviewId": "R7",
    "productId": "EXAMPLE-BLENDER",
    "reviewTitle": null,
    "integrityRiskScore": 0,
    "signalLevel": "no listed signals found",
    "signalCount": 0,
    "categoriesFlagged": [],
    "signals": [],
    "rating": 2,
    "reviewDate": "2026-10-06",
    "wordCount": 9,
    "checkedAt": "2026-10-30T18:52:06+00:00",
    "disclaimer": "Automated screen for warning signs described in the FTC Consumer Reviews and Testimonials..."
  }
]

How much does it cost to check reviews?

This Actor uses pay-per-event pricing: $0.0005 per review saved plus a $0.00005 start fee per run, plus small platform usage paid by you (a few hundredths of a cent for small runs). That's about $0.50 for 1,000 reviews and $5 for 10,000 reviews plus usage. With Only save rows with findings on, reviews with a score of 0 aren't saved or charged. You can set a maximum cost per run in the run options.

Use it through the API, integrations or AI agents

• Run it and get results in one call: https://api.apify.com/v2/acts/ultimatemc~review-integrity-checker/run-sync-get-dataset-items
• Use it from AI agents through the Apify MCP server: https://mcp.apify.com?tools=ultimatemc/review-integrity-checker
• Schedule it after a daily scraper run, send flagged rows to Slack or email with webhooks, or connect it to Make, Zapier, n8n or Google Sheets.
• Chain it after a review scraper by passing the scraper's dataset ID.

How it works

Signals are deterministic English text patterns and simple statistics based on the FTC Trade Regulation Rule on the Use of Consumer Reviews and Testimonials (16 CFR 465, in effect since 2024-10-21) and the FTC Endorsement Guides (16 CFR 255), checked on 2026-10-30. No AI and no outside calls are used, so the same input gives the same result. Each signal type counts once per review and the score is the sum of the weights, capped at 100. Levels: strong signals (40+), some signals (15-39), weak signals (1-14), no listed signals found (0). Copy and burst checks only compare reviews in the same run.

Sources:

• 16 CFR Part 465: Use of Consumer Reviews and Testimonials
• FTC: Final rule banning fake reviews and testimonials (Aug 2024)
• 16 CFR Part 255: Endorsement Guides
• Trustpilot guidelines for reviewers

FAQ

Does a high score mean the review is fake?

No. It means the text or pattern matches something the FTC rule or guides describe. A disclosed free sample can be a perfectly honest review. Use the score to decide which reviews a person should look at.

Which languages does it support?

English only. Other languages will mostly score 0, except for the copy and burst checks.

Is it AI?

No. It's a fixed, weighted rule set, so results are repeatable and explainable, and runs cost $0.0005 per review.

Why didn't the burst check run?

It needs date and productId on the reviews and at least the Min reviews for a burst on one day.

Does it output reviewer names?

No. The reviewer field is only used internally to tell reviewers apart and is never saved.

Limitations

• English text patterns only; paraphrased incentives can be missed.
• Copy and burst checks only see the reviews in one run.
• It can't see platform data like verified-purchase status or reviewer history unless your scraper includes it in the text.
• Patterns reflect the FTC rule and guides as of 2026-10-30.

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Found a false positive or a missed pattern? Open an issue on the Issues tab with the review text (remove personal details), and we'll review the rule.