Fakespot: Over the past few years, more and more people have started using the internet to make purchases of goods and services. However, the surge in internet purchasing has also contributed to a rise in fraudulent reviews. These reviews may mislead customers and even be hazardous. Reviews that are not written by genuine users of a product or service are considered fake reviews. They may be written by the business itself, by staff members. Sometime you can also find these kind of reviews from independent businesses who have been paid to leave favourable evaluations. You may find these phoney reviews on a number of websites, including TripAdvisor, Yelp, and Amazon.
There are many issues that can arise from trusting reviews that have been fabricated. First, they can trick customers into purchasing a service or good that isn’t as fantastic as it seems. Customers may feel cheated out of their money and frustrated with the product as a result. Second, businesses that have gotten a lot of fake reviews might not be as good as they seemed at first. Because the false comments made people think less of them. Last but not least, fake reviews can be used to trick search engines, making it harder for people to find honest opinions.
Fake Review: How to Spot Them?
Finding fake reviews can be challenging, but there are a few ways to spot them
- Check for repetitions: testimonials that all sound the same might be fabricated. Reviews with mainly favorable or overly enthusiastic comments should also raise red flags.
- Investigate the reviewer’s background by looking at their previous reviews: Check the reviewer’s background to check if they have experience with product reviews. It’s possible that a reviewer is trying to trick you into buying something by leaving only a few ratings or by only leaving reviews for one product.
- Make sure you have all the facts: Look for reviews that explain the product’s features and use in great detail. False reviews frequently fail to provide concrete examples.
- You may tell if a review was written by the same person or persons by looking at when it was posted. Reviews posted on the same day or within a few days of each other are likely to have been written by the same person.
- To determine whether a review is genuine or not, you can use a review checker that compares it to others that have been written about the same product.
- Be wary of reviews that are all praise, as this could indicate that the reviewer has something to hide, especially if there are no critical remarks to counteract the favorable ones.
Overcoming fake reviews is not an easy task. It takes a lot of experience and judgmental capabilities to spot on a fake review. Sometimes even experts fell into this bottomless pit. This is where technology might help you to survive.
Fake Review Detection Using Artificial Intelligence (AI)
There are a variety of AI-based methods that can be used to find fake reviews. Some of the most common include:
- Natural language processing (NLP) is a method for deciphering the language of reviews for anomalies or trends that could point to their falsity. Reviews written in a consistent style can be attributed to the same reviewer thanks to natural language processing
- Machine Learning: By examining massive amounts of data, machine learning systems can be taught to spot bogus reviews. These algorithms can examine the reviews’ language, sentiment, and other characteristics—including the reviewer’s past—in order to spot phony ones.
- Deep Learning: Neural networks and other deep learning algorithms can examine review content for telltale patterns that can suggest a review is false. Furthermore, these algorithms can be trained on a huge dataset to improve their accuracy over time.
- Hybrid approaches: Detecting fraudulent reviews can be made easier by combining two or more methods stated above.
Fakespot: How it works?
There is a chrome extension called Fakespot which might come handy to spot fake reviews. Fakespot is a website and browser plugin that analyzes review data to assist consumers spot potentially fraudulent reviews. It works based on AI.
Fakespot looks at reviews for products and services sold on sites like Amazon, Walmart, Shopify and Best Buy. It does this by using a special algorithm. The algorithm looks for patterns and differences in the way reviews are written, as well as other things like the number of reviews and when they were written, to figure out how good and trustworthy they are.
When a user searches for a product on Fakespot, the site will show the product’s “Fakespot Grade”. It is based on how many good reviews it has. For example, a grade of “A” would mean that most of the reviews for that product are reliable. If it it is grade of “F” would mean that most of the reviews are not reliable or are fake.
Users can also use the Fakespot browser extension to analyze reviews on the e-commerce website directly without the need to visit the Fakespot website. If you are mobile user, then Fakespot has got an application as well. It will also show the grade of the product page and provide a breakdown of the reviews by reliability.
Fakespot’s algorithm is not perfect, and the site does not guarantee that its analysis is correct. But it can be a useful tool when combined with other ways to do research to help people make better buying decisions. This extension in some cases really helps a lot. For instance, I said spotting a fake review is not a cake walk. It requires a lot of experience and judgmental capabilities. This extension will provide you that. I, personally liked the way this extension is using AI capabilities such as NLP and Machine Learning.
I really loved using the “Fakespot Adjusted Rating” option. It analyses tens and thousands of comments to give you the real adjusted rating finally. Even the data that is returning in the form of “Overview” has helped me a lot in product purchasing decisions.
It is quite evident that Fakespot is improving a lot. The app not only scans a lot of products, it is also taking any new products given to it in to the account. One thing I observed is that this application works better with the amount of ratings. If the amount of ratings are more, Fakespot is returning good results. If they are not present sufficiently, then the engines are flagging the product as “Untrustworthy”. Apart from this everything works fine with the application.
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