
Show HN: Fakespot – identify fake reviews  Amazon - dukefall
http://www.fakespot.com
======
dukefall
Fellow hackers, I created this site that analyzes reviews of an Amazon
product.

The algorithm analyzes language and many other variables improving over time
due to machine learning implementation.

Primary aim is to distinguish fake reviews (aka, reviews that were paid for
the purpose of inflating a product ranking) from the legit reviews.

~~~
smt88
How do you test? Like how are you _certain_ that some reviews are fake and
others are real?

~~~
dukefall
Testing is done by extracting the review text, author's other reviews that
includes natural language processing, and the reviewer profile variables. As
mentioned, main focus was to determine paid reviews that some companies
purchase and there are many believe, it or not. The machine learning
implementation stores validated "fake reviews" in the database to keep as a
profile to test against. Users can also vote if they think the grade
calculated is fair or not, according to their interpretation of the listed
reviews. Those votes are also utilized in the machine learning algo.

~~~
aayushamicus
I saw something similar being claimed by Yelp in one of their promotional
videos.
[https://www.youtube.com/watch?v=jbkQtW8A408](https://www.youtube.com/watch?v=jbkQtW8A408)
And after watching it had the same doubts that I have now:

Aren't most paid reviews done by agencies which have either 1) An army of
employees who have old profiles with several reviews and are trained to give a
balanced review? Why is their writing style likely to be any different from
real reviewers? There is a high probability of false negatives. This might
just weed out the bad freelancers who put no thought in writing such reviews.

Or 2) They will pay real reviewers who are influencers to give them favourable
reviews. And these you cannot and should not remove in any case.

Also, What check do you have against false positives?

And what do you think of solutions which rely and actual verified human
intelligence? Like: [http://www.inc.com/magazine/201404/liz-welch/stella-
service-...](http://www.inc.com/magazine/201404/liz-welch/stella-service-
rates-your-customer-service.html)

~~~
dukefall
Yes! I have seen the stuff Yelp is doing. Reviews are definitely integral to
Yelp so they must eliminate those fabricated reviews to maintain their
reputation otherwise no one would be using their website.

1) You are correct, a large number of the fake reviews are actually a result
of agencies that offer their services to manufacture high star reviews to
increase their product ranking. Now, these people use bots (or an army of
trained monkeys) for most of their review entries and Amazon has in the past
detected them. However in the past year or so I've noticed a lot of fake
reviews still lingering in Amazon and that is a shame as a lot of the time
they mislead consumers. This is why I created the site as I have fallen victim
to it too. Just take a look at the health supplements section of Amazon and
look at the amount of dishonest marketing some companies are doing.

For case 2), at the present moment Fakespot would be unable to distinguish
such a scenario where a reputable reviewer was paid by a company because that
would be indeed a "real" review written by a real person. However, a large
chunk of the analysis algorithm does look at the language and if the review
text is extremely positive and not very detailed to relevant to the product,
it will raise a flag.

3) For false positives, the machine learning implementation records votes and
modifies itself to eliminate them.

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secfirstmd
This is a cool idea for Amazon but would be really really useful for
TripAdvisor. That thing is so blatantly riddled with some people gaming it. I
myself have reported people for being ridiculously obvious about it.

