Paste a review — we estimate manipulation likelihood from hype, emotional overload and lack of specifics, with no model. A rough guide.
Paste a review — we estimate manipulation likelihood from hype, emotional overload and lack of specifics, with no model. A rough guide. Free with no sign-up, and the analysis runs inside your browser — files never leave your device. On our public gate (30 real + 52 AI images) it measured 96.3% at original quality and 91.5% on recompressed copies, with zero real photos confidently mislabelled as AI. Treat it as an indicator, not proof: the newest commercial generators evade every detector.
Estimate whether a shopping, app-store or restaurant review reflects real experience or a paid, exaggerated or fabricated one — using the language of the text. It flags the linguistic signals you miss when you look only at the star rating.
Fake reviews pack in emotional adjectives and superlatives ("the best", "life-changing", "highly recommend", "perfect"). People who actually used the product tend to mention small annoyances too (shipping, size, smell) alongside the good.
Lack of specificity is a key signal. "Really good, so satisfied" never explains what about the product is good, or why. And when many reviews reuse the same sentence template, mass templated writing is likely.
The score reflects "likelihood of manipulation/exaggeration", combining hype density, emotional bias, lack of specificity and templated structure. Detected hype phrases are shown as tags.
Watch the pattern more than any single review's score. Extreme 5-star (or 1-star) bursts in a short window, or accounts that only ever praise one product category, are far stronger signals than one sentence.
No. This tool only reads signals of exaggeration in the wording. Genuinely good products also rate highly — use the score only as a reason to look twice.
One or two sentences lack enough signal and the statistics are unstable. Reading many reviews as a pattern is more accurate.