ARES 70020-1/2-Inch Drive 17MM Non-Marring Lug Nut Socket - Protective Sleeve Protects Custom Rims & Lug Nuts from Damage - Color Coded & Laser Etched for Easy Identification

ARES 70020-1/2-Inch Drive 17MM Non-Marring Lug Nut Socket - Protective Sleeve Protects Custom Rims & Lug Nuts from Damage - Color Coded & Laser Etched for Easy Identification

ASIN: B017MXJHO2
Analysis Date: Sep 26, 2025

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Review Analysis Results

B
Authenticity Grade
18.00%
Fake Reviews
3.13
Original Rating
2.80
Adjusted Rating

Analysis Summary

The review set shows a mostly authentic pattern with some minor concerns. The reviews display natural variation in ratings (5-star to 1-star), specific technical details about vehicle applications (Honda, BMW, bz4X), and realistic usage scenarios (wheel swaps, dealership experiences). The language is generally natural with appropriate technical terminology. However, there are some minor red flags including one duplicate review (R29DEC19S5RA1B appears twice with identical text) and a few reviews with slightly formulaic phrasing. The mix of positive and negative experiences with specific failure descriptions suggests genuine user feedback. The 'V' verification indicators throughout add credibility. Overall, this appears to be a legitimate product review set with a low percentage of potentially inauthentic content.

Review Statistics

356
Total Reviews on Amazon
-0.33
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 (2.80 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 (3.13 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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