Know if those Amazon reviews are real
Free Amazon fake review checker. Paste a product link, ASIN, or name and we grade review authenticity from verified-purchase ratio, rating skew, posting bursts, and copy-pasted text. An estimate, not an accusation.
Searching 42 platforms in parallel
Three steps, zero learning curve
Paste a URL, hit run, get structured data. That's the whole workflow.
- 01
How does it spot fake reviews?
We read the most recent reviews Amazon returns and grade four signals: the verified-purchase ratio, how skewed the stars are, whether reviews arrived in a single burst, and whether the wording looks copy-pasted.
- 02
What do you actually show me?
A trust grade from A to F, a 0 to 100 score, a breakdown of each signal, the full rating spread, and a sample of the real reviews so you can judge for yourself.
- 03
Is this proof a review is fake?
No. It's an authenticity estimate from a small recent sample. A low grade means the reviews are worth a closer read, not that anyone faked them.
Use it in your code in seconds
Same engine, exposed as a clean REST API. One key works for every platform — and every response uses the same schema.
One key, every platform
Authenticate once. Hit TikTok, Instagram, YouTube, Reddit, and 17 more — all from the same key.
Same shape, every response
Identical schema across platforms. Write your integration once and never adapt to a new payload.
Drop into any agent
Cleanly typed JSON, ready for LLM tool-calls, RAG pipelines, and automation flows.
import requests
response = requests.get(
'https://www.socialcrawl.dev/v1/tiktok/profile',
params={'handle': 'charlidamelio'},
headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json(){
"success": true,
"platform": "tiktok",
"data": {
"author": {
"username": "charlidamelio",
"followers": 152400000
},
"engagement": {
"likes": 12400000000,
"engagement_rate": 0.087
},
"metadata": {
"language": "en",
"content_category": "lifestyle"
}
}
}What is the world really saying?
Type a question. Twelve walled gardens search at the same time. AI gives you the brief — the source posts stay one click away.
For builders → MCP, skills, and the APIReal ways teams use this tool
A few patterns we see from marketers, researchers, and builders shipping with SocialCrawl.
Can I trust this product's rating?
Before you click buy, see whether that 4.7-star average is built on verified buyers or a wall of identical five-star reviews.
0
signals checked
per product
0A-F
trust grade
at a glance
How clean is a competitor's review profile?
Check any listing for seeded reviews and posting bursts, then prove the pattern with the verified-purchase ratio.
0
guesswork
evidence only
0
point trust scale
0 to 100
Count the real buyers
See what share of reviews carry Amazon's Verified Purchase badge, the strongest signal a review is genuine.
Spot five-star floods
A natural product has a spread of ratings. A wall of five stars with no critical reviews is a tell.
Catch copy-paste reviews
Templated, near-identical reviews collapse together so seeded wording stops hiding in plain sight.
Frequently asked questions
Can't find what you're looking for? Talk to our team or ask the AI agent below
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