Y-NOT Silicone Breast Forms, Self-Adhesive Fake Boobs with Medical Grade Silicone, Reusable Fake Breast Silicone Boob

Y-NOT Silicone Breast Forms, Self-Adhesive Fake Boobs with Medical Grade Silicone, Reusable Fake Breast Silicone Boob

ASIN: B019MIHSFU
Analysis Date: Nov 7, 2025 (re-analyzed Nov 7, 2025)

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Review Analysis Results

D
Authenticity Grade
42.00%
Fake Reviews
3.87
Original Rating
3.10
Adjusted Rating

Analysis Summary

The review set shows moderate signs of artificial manipulation with several concerning patterns. The rating distribution is heavily polarized with 7 five-star reviews (50%) and 4 one-star reviews (29%), creating a classic 'J-shaped' distribution often seen in manipulated review sets. Multiple reviews contain suspicious elements including: review R1UU1BEKXZSPJP appears twice verbatim (clear duplication), several reviews contain irrelevant content (R2MRVN79SEMC6J discusses drag culture and Sasha Valour unrelated to product performance), and R3331GWQ7YK6SM contains non-English text with no product evaluation. However, many reviews show authentic characteristics including detailed usage experiences, specific product complaints about adhesive quality and sizing accuracy, and varied writing styles. The presence of moderate negative reviews (3-star and 4-star) provides some credibility balance.

Review Statistics

2,412
Total Reviews on Amazon
-0.77
Rating Difference

Price Analysis

Price analysis pending

Price insights will be available shortly.

Understanding This Analysis

What does Grade D mean?

This product has significant review authenticity issues. Many reviews show patterns consistent with fake or incentivized reviews. Exercise caution.

Adjusted Rating Explained

The adjusted rating (3.10 stars) represents what we estimate this product's rating would be if fake reviews were removed. This product's adjusted rating is lower than Amazon's displayed rating (3.87 stars), suggesting positive fake reviews may be inflating the score.

How We Detect Fake Reviews

Our AI analyzes multiple factors: language patterns (generic vs. specific), reviewer behavior (history, timing), temporal anomalies (review clusters), verification status, sentiment authenticity, and statistical outliers. No single factor determines a review is fake - we look at the combination of signals.

Important Limitations

No automated system is perfect. Sophisticated fake reviews can evade detection, and some genuine reviews may be incorrectly flagged. Use this analysis as one data point in your purchasing decision, not the only factor. Reading actual review content yourself is always valuable.

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