Can i trust half.com

In fact, in April, Amazon sued another round of companies that are accused of selling fraudulent reviews. But by the time those companies are caught, their clients have already made a bunch of sales, and the fraudulent reviewers will likely pop up again under new names to repeat the process. Want to know more? Wirecutter headphones editor Lauren Dragan talks to Marketplace Tech about compensated Amazon reviews and how to tell real crowdsourced opinions from astroturfing.

You have a few ways to suss out what may be a fake review. The easiest way is to use Fakespot. This site allows you to paste the link to any Amazon product and receive a score regarding the likelihood of fake reviews. For example, we ran an analysis on some headphones we found during a recent research sweep for our guide about cheap in-ear headphones.

We corresponded with an official spokesperson for Fakespot to get a better idea of where these results come from. He said:. The quick answer is that every analysis does two simultaneous things: we analyze every single review posted and we review each reviewer and every review that reviewer has ever posted on that account.

We take all that data and run it through our proprietary engine which grades everything and looks for patterns. Every fake reviewer has patterns. And the more data we collect via analyses completed, the more our engine is able to adapt and learn. The secret sauce is not only in the engine but the ability to run the data in the quickest amount of time possible; ensuring swift delivery of an accurate product.

To get some perspective, we spoke with Bing Liu, a professor in the department of computer science at the University of Illinois at Chicago , whose focuses include sentiment analysis, opinion mining, and lifelong machine learning.

He has written textbooks on the subjects. We wanted to know his opinion on whether it is possible for a program or group of programs to evaluate reviews and correctly determine their validity. It is hard to say without knowing their techniques. The problem with this task is that there is often no hard proof that the detection is actually correct unless the author of the actual fake reviews not made up fake reviews from a review hosting site confirms it. Of course, it is easier if the company actually hosts reviews e.

A lot of unusual behaviors can be detected. Unfortunately, such data is not available to people outside the site. However, you can make educated guesses. All of that aside, we had a similar opinion when we read the Rxvoit reviews ourselves, and we can tell you a few factors that we use when evaluating customer reviews. Well, first of all, we noticed that a lot of the positive reviews happened within a few days of each other.

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