New Algorithm Recognizes Both Good and Bad Fake Reviews (thestack.com)
An anonymous reader writes: Researchers from the university of Sao Paolo have developed an algorithm able to identify both good and bad online reviews in the massive daily chatter of millions of peer-community posts, and in lateral mendacities at social network sites such as Google+ and Facebook reposts and 'likes'. Two of the datasets tested in the research were from Amazon, which has a vested interest in restoring the reputation of its community reviews, and has recently taken action on the matter.
With Siskel and Ebert now gone, my bot is picking up the slack: it gives this concept two thumbs down.
While I have spent a lot of time studying algorithms, I still like to look for new algorithms from time to time. This algorithm is definitely one of the better algorithms I have come across in my life.
When I first started studying the algorithm, I wasn't sure whether it was for me. However, after the first few pages, the algorithm began to grab my attention, and soon I was finding its average and worst case complexity and formally proving the correctness of several subroutines. I was astounded to find that it was at least as good as all the other algorithms I'd found over my twenty years' experience.
While I wouldn't recommend this algorithm to a beginner without guidance, the journeyman will soon find themselves reaching master by applying this algorithm. The master cannot fail to learn something new from it.
New Algorithm Recognizes Both Good and Bad (Fake Reviews): The algorithm recognizes good fake reviews and bad fake reviews, but not real reviews.
New Algorithm Recognizes Both Good and (Bad Fake) Reviews: The algorithm recognizes good reviews and bad fake reviews, but no real bad reviews or fake good reviews.
New Algorithm Recognizes Both (Good and (Bad Fake) Reviews): The algorithm recognizes a particular good review and a particular bad fake review.
New Algorithm Recognizes Both (Good and Bad (Fake Reviews)): The algorithm recognizes a particular good review and a particular bad review.
New Algorithm Recognizes Both Good and Bad Fake Reviews: The algorithm recognizes ruthlessly pedantic slashdot comments.
Just return true all the time. Sure there will be a few false positives but not enough to throw the results off by much.
It combines orgasmic whitefish essences and loud mustard overtones with a stale nacho aftertaste.
Part of what makes me come back to Amazon's site to buy products is the humorous fake reviews for things that are absurdly expensive or seems like it shouldn't exist.
Why can't we just have moderation? Most of the poor (low quality, as opposed to negative) reviews are easy to spot; knuckleheads that break things two seconds after they get it out of the package; half the things they buy are mysteriously "DOA." Moderation enables your smart customers to punish your idiot customers.
Just spotted one of these today on eham.net. A beautiful hand made band pass filter rated 4.2/5 instead of the 5/5 it deserves because — years ago — some moron used it on the wrong band for the wrong purpose and rated it 0/5.
I thought that was what the "Was this review helpful to you? YES/NO/Report Abuse" options were for?
Effective community policing needs an involved community like slashdot. Even here, where nobody's trying to sell you anything other than an opinion, the sock-puppet modding can get out of hand sometimes...
"I love animals! Some are cute, others are tasty, what's not to like?" - Betsy Schroeder, Jeopardy contestant
If you haven't seen this, there is a service that looks at reviews to find if they are fake or not: http://fakespot.com/ From what I can gather about them, they also use clustering algorithms to detect the fake Amazon reviews.
actually "Universidade de São Paulo", located in the city and state of the same name. I get that you anglophones don't go well with tildes [and for that you are excused], but don't change so drastically the name of my town/state.
The only way Amazon could restore the reputation of their reviews would be to change from the bad it's always been to something better (because it couldn't get any worse), then make it bad again.
*** this post has been recognized as fake and removed by the new automatic fake post identifier ***
Well, you're obviously trolling, at least a little, but still deserve a reply. Any deterministic algorithm can be bested simply by determining what the algorithm does and then doing something different. So, yes, this isn't actually going to work as a permanent solution. It may work on past reviews, it may work for some time in the future, and it may work against those unwilling to put an effort into their work. However, someone will, almost certainly, and find the flaws. There is a technical term for this but, suffice to say, it boils down to there being no such thing as a free lunch.
Maths, explained conceptually, are a good thing. *nods* I'm not good at analogies but think of it like a law. No law can be without loopholes or it would be draconian and not have a desirable outcome. Yet, each loophole have potential abuses. There is no such thing as a perfect law which will give you ideal results in all situations.
Or, if you'd prefer, an automobile that is optimized for efficiency will not be good for hauling around extra weight - it will even be less efficient than an automobile designed to carry such a load. One can not create an automobile that will function ideally in all situations. Even if you could create such a thing, someone would just come along with a different use-case where such failed. It may take someone a moment to find that use-case but it will happen.
"So long and thanks for all the fish."
https://xkcd.com/810/
Long live the Speaker Bracelet
Rolo D. Monkey
And yet spam email detection has got so good that I almost never get a spam.
And my ad-blocker is so good that I almost never see an ad.
So I don't think much to your theory of inevitable defeat.
No? Then why'd you use the word "almost?" Did you block APK's ad yet? Does your spam filtering never get a false positive, never let spam through?
And no, it's not my theory. You could see it as an off-shot of things like this: https://en.wikipedia.org/wiki/...
It's a pretty basic idea and doesn't really require that you agree with it. If you can prove them wrong then, well, you'd be famous.
"So long and thanks for all the fish."
Because almost is very useful. If a site like Amazon can kill 99% of fake reviews that's an extremely worthwhile thing to do. It doesn't have to be perfect.
And spam filtering does a hell of a lot better than 99% these days. I honestly can't remember when I last got a real email spam. It may be years.
And no, it's not my theory. You could see it as an off-shot of things like this: https://en.wikipedia.org/wiki/..
Then either it's a crap theory or you are misusing it. But there's no fame in pointing out that spam filtering works. It's surprising you haven't realised it.