USB C Female to USB Male Adapter (2-Pack),Type C to USB A Charger Cable Adapter, Compatible with iPhone 16 15 15 Pro Max,iPad 2018,Samsung Galaxy Note 10 S22 S21 S20+ Plus Ultra,Google Pixel 4 3

USB C Female to USB Male Adapter (2-Pack),Type C to USB A Charger Cable Adapter, Compatible with iPhone 16 15 15 Pro Max,iPad 2018,Samsung Galaxy Note 10 S22 S21 S20+ Plus Ultra,Google Pixel 4 3

ASIN: B07VCZV3R4
Analysis Date: Sep 23, 2025

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Review Analysis Results

B
Authenticity Grade
18.00%
Fake Reviews
4.40
Original Rating
4.00
Adjusted Rating

Analysis Summary

The review set shows mostly authentic characteristics with minor concerns. The product has a realistic rating distribution (14 five-star, 1 four-star, and 2 one-star reviews), with the negative reviews providing specific, credible complaints about missing items. Most reviews contain detailed, varied usage scenarios that align with legitimate customer experiences. However, there are a few overly simplistic 5-star reviews that lack detail, and the overall text patterns show some repetition in phrasing. The presence of verified purchase tags and the natural distribution of ratings suggest this is primarily a genuine review set with a small percentage of potentially low-quality or incentivized reviews.

Review Statistics

3,266
Total Reviews on Amazon
-0.40
Rating Difference

Price Analysis

Price analysis pending

Price insights will be available shortly.

Understanding This Analysis

What does Grade B mean?

This product has good review authenticity with minor concerns. While most reviews appear genuine, we detected some patterns that warrant mild caution.

Adjusted Rating Explained

The adjusted rating (4.00 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 (4.40 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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